Effect of an evidence-based website on healthcare usage: an interrupted time-series study.

Science.gov (United States)

Spoelman, Wouter A; Bonten, Tobias N; de Waal, Margot W M; Drenthen, Ton; Smeele, Ivo J M; Nielen, Markus M J; Chavannes, Niels H

2016-11-09

Healthcare costs and usage are rising. Evidence-based online health information may reduce healthcare usage, but the evidence is scarce. The objective of this study was to determine whether the release of a nationwide evidence-based health website was associated with a reduction in healthcare usage. Interrupted time series analysis of observational primary care data of healthcare use in the Netherlands from 2009 to 2014. General community primary care. 912 000 patients who visited their general practitioners 18.1 million times during the study period. In March 2012, an evidence-based health information website was launched by the Dutch College of General Practitioners. It was easily accessible and understandable using plain language. At the end of the study period, the website had 2.9 million unique page views per month. Primary outcome was the change in consultation rate (consultations/1000 patients/month) before and after the release of the website. Additionally, a reference group was created by including consultations about topics not being viewed at the website. Subgroup analyses were performed for type of consultations, sex, age and socioeconomic status. After launch of the website, the trend in consultation rate decreased with 1.620 consultations/1000 patients/month (pHealthcare usage decreased by 12% after providing high-quality evidence-based online health information. These findings show that e-Health can be effective to improve self-management and reduce healthcare usage in times of increasing healthcare costs. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Effect of an evidence-based website on healthcare usage: an interrupted time-series study

Science.gov (United States)

Spoelman, Wouter A; Bonten, Tobias N; de Waal, Margot W M; Drenthen, Ton; Smeele, Ivo J M; Nielen, Markus M J; Chavannes, Niels H

2016-01-01

Objectives Healthcare costs and usage are rising. Evidence-based online health information may reduce healthcare usage, but the evidence is scarce. The objective of this study was to determine whether the release of a nationwide evidence-based health website was associated with a reduction in healthcare usage. Design Interrupted time series analysis of observational primary care data of healthcare use in the Netherlands from 2009 to 2014. Setting General community primary care. Population 912 000 patients who visited their general practitioners 18.1 million times during the study period. Intervention In March 2012, an evidence-based health information website was launched by the Dutch College of General Practitioners. It was easily accessible and understandable using plain language. At the end of the study period, the website had 2.9 million unique page views per month. Main outcomes measures Primary outcome was the change in consultation rate (consultations/1000 patients/month) before and after the release of the website. Additionally, a reference group was created by including consultations about topics not being viewed at the website. Subgroup analyses were performed for type of consultations, sex, age and socioeconomic status. Results After launch of the website, the trend in consultation rate decreased with 1.620 consultations/1000 patients/month (p<0.001). This corresponds to a 12% decline in consultations 2 years after launch of the website. The trend in consultation rate of the reference group showed no change. The subgroup analyses showed a specific decline for consultations by phone and were significant for all other subgroups, except for the youngest age group. Conclusions Healthcare usage decreased by 12% after providing high-quality evidence-based online health information. These findings show that e-Health can be effective to improve self-management and reduce healthcare usage in times of increasing healthcare costs. PMID:28186945

Enhanced Interrupt Response Time in the nMPRA based on Embedded Real Time Microcontrollers

Directory of Open Access Journals (Sweden)

GAITAN, N. C.

2017-08-01

Full Text Available In any real-time operating system, task switching and scheduling, interrupts, synchronization and communication between processes, represent major problems. The implementation of these mechanisms through software generates significant delays for many applications. The nMPRA (Multi Pipeline Register Architecture architecture is designed for the implementation of real-time embedded microcontrollers. It supports the competitive execution of n tasks, enabling very fast switching between them, with a usual delay of one machine cycle and a maximum of 3 machine cycles, for the memory-related work instructions. This is because each task has its own PC (Program Counter, set of pipeline registers and a general registers file. The nMPRA is provided with an advanced distributed interrupt controller that implements the concept of "interrupts as threads". This allows the attachment of one or more interrupts to the same task. In this context, the original contribution of this article is to presents the solutions for improving the response time to interrupts when a task has attached a large number of interrupts. The proposed solutions enhance the original architecture for interrupts logic in order to transfer control, to the interrupt handler as soon as possible, and to create an interrupt prioritization at task level.

Prevention of brachial plexus injury-12 years of shoulder dystocia training: an interrupted time-series study.

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Crofts, J F; Lenguerrand, E; Bentham, G L; Tawfik, S; Claireaux, H A; Odd, D; Fox, R; Draycott, T J

2016-01-01

To investigate management and outcomes of incidences of shoulder dystocia in the 12 years following the introduction of an obstetric emergencies training programme. Interrupted time-series study comparing management and neonatal outcome of births complicated by shoulder dystocia over three 4-year periods: (i) Pre-training (1996-99), (ii) Early training (2001-04), and (iii) Late training (2009-12). Southmead Hospital, Bristol, UK, with approximately 6000 births per annum. Infants and their mothers who experienced shoulder dystocia. A bi-monthly multi-professional 1-day intrapartum emergencies training course, that included a 30-minute practical session on shoulder dystocia management, commenced in 2000. Neonatal morbidity (brachial plexus injury, humeral fracture, clavicular fracture, 5-minute Apgar score dystocia (resolution manoeuvres performed, traction applied, head-to-body delivery interval). Compliance with national guidance improved with continued training. At least one recognised resolution manoeuvre was used in 99.8% (561/562) of cases of shoulder dystocia in the late training period, demonstrating a continued improvement from 46.3% (150/324, P dystocia. © 2015 Royal College of Obstetricians and Gynaecologists.

Evaluating the impact of flexible alcohol trading hours on violence: an interrupted time series analysis.

Directory of Open Access Journals (Sweden)

David K Humphreys

Full Text Available On November 24(th 2005, the Government of England and Wales removed regulatory restrictions on the times at which licensed premises could sell alcohol. This study tests availability theory by treating the implementation of Licensing Act (2003 as a natural experiment in alcohol policy.An interrupted time series design was employed to estimate the Act's immediate and delayed impact on violence in the City of Manchester (Population 464,200. We collected police recorded rates of violence, robbery, and total crime between the 1st of February 2004 and the 31st of December 2007. Events were aggregated by week, yielding a total of 204 observations (95 pre-, and 109 post-intervention. Secondary analysis examined changes in daily patterns of violence. Pre- and post-intervention events were separated into four three-hour segments 18∶00-20∶59, 21∶00-23.59, 00∶00-02∶59, 03∶00-05∶59.Analysis found no evidence that the Licensing Act (2003 affected the overall volume of violence. However, analyses of night-time violence found a gradual and permanent shift of weekend violence into later parts of the night. The results estimated an initial increase of 27.5% between 03∶00 to 06∶00 (ω = 0.2433, 95% CI = 0.06, 0.42, which increased to 36% by the end of the study period (δ = -0.897, 95% CI = -1.02, -0.77.This study found no evidence that a national policy increasing the physical availability of alcohol affected the overall volume of violence. There was, however, evidence suggesting that the policy may be associated with changes to patterns of violence in the early morning (3 a.m. to 6 a.m..

Improving prehospital trauma care in Rwanda through continuous quality improvement: an interrupted time series analysis.

Science.gov (United States)

Scott, John W; Nyinawankusi, Jeanne D'Arc; Enumah, Samuel; Maine, Rebecca; Uwitonze, Eric; Hu, Yihan; Kabagema, Ignace; Byiringiro, Jean Claude; Riviello, Robert; Jayaraman, Sudha

2017-07-01

Injury is a major cause of premature death and disability in East Africa, and high-quality pre-hospital care is essential for optimal trauma outcomes. The Rwandan pre-hospital emergency care service (SAMU) uses an electronic database to evaluate and optimize pre-hospital care through a continuous quality improvement programme (CQIP), beginning March 2014. The SAMU database was used to assess pre-hospital quality metrics including supplementary oxygen for hypoxia (O2), intravenous fluids for hypotension (IVF), cervical collar placement for head injuries (c-collar), and either splinting (splint) or administration of pain medications (pain) for long bone fractures. Targets of >90% were set for each metric and daily team meetings and monthly feedback sessions were implemented to address opportunities for improvement. These five pre-hospital quality metrics were assessed monthly before and after implementation of the CQIP. Met and unmet needs for O2, IVF, and c-collar were combined into a summative monthly SAMU Trauma Quality Scores (STQ score). An interrupted time series linear regression model compared the STQ score during 14 months before the CQIP implementation to the first 14 months after. During the 29-month study period 3,822 patients met study criteria. 1,028 patients needed one or more of the five studied interventions during the study period. All five endpoints had a significant increase between the pre-CQI and post-CQI periods (pRwanda. This programme may be used as an example for additional efforts engaging frontline staff with real-time data feedback in order to rapidly translate data collection efforts into improved care for the injured in a resource-limited setting. Copyright © 2017 Elsevier Ltd. All rights reserved.

The time-course of recovery from interruption during reading: eye movement evidence for the role of interruption lag and spatial memory.

Science.gov (United States)

Cane, James E; Cauchard, Fabrice; Weger, Ulrich W

2012-01-01

Two experiments examined how interruptions impact reading and how interruption lags and the reader's spatial memory affect the recovery from such interruptions. Participants read paragraphs of text and were interrupted unpredictably by a spoken news story while their eye movements were monitored. Time made available for consolidation prior to responding to the interruption did not aid reading resumption. However, providing readers with a visual cue that indicated the interruption location did aid task resumption substantially in Experiment 2. Taken together, the findings show that the recovery from interruptions during reading draws on spatial memory resources and can be aided by processes that support spatial memory. Practical implications are discussed.

Thinking aloud in the presence of interruptions and time constraints

DEFF Research Database (Denmark)

Hertzum, Morten; Holmegaard, Kristin Due

2013-01-01

and time constraints, two frequent elements of real-world activities. We find that the presence of auditory, visual, audiovisual, or no interruptions interacts with thinking aloud for task solution rate, task completion time, and participants’ fixation rate. Thinking-aloud participants also spend longer......Thinking aloud is widely used for usability evaluation and its reactivity is therefore important to the quality of evaluation results. This study investigates whether thinking aloud (i.e., verbalization at levels 1 and 2) affects the behaviour of users who perform tasks that involve interruptions...... responding to interruptions than control participants. Conversely, the absence or presence of time constraints does not interact with thinking aloud, suggesting that time pressure is less likely to make thinking aloud reactive than previously assumed. Our results inform practitioners faced with the decision...

Effect of nocturnal sound reduction on the incidence of delirium in intensive care unit patients: An interrupted time series analysis.

Science.gov (United States)

van de Pol, Ineke; van Iterson, Mat; Maaskant, Jolanda

2017-08-01

Delirium in critically-ill patients is a common multifactorial disorder that is associated with various negative outcomes. It is assumed that sleep disturbances can result in an increased risk of delirium. This study hypothesized that implementing a protocol that reduces overall nocturnal sound levels improves quality of sleep and reduces the incidence of delirium in Intensive Care Unit (ICU) patients. This interrupted time series study was performed in an adult mixed medical and surgical 24-bed ICU. A pre-intervention group of 211 patients was compared with a post-intervention group of 210 patients after implementation of a nocturnal sound-reduction protocol. Primary outcome measures were incidence of delirium, measured by the Intensive Care Delirium Screening Checklist (ICDSC) and quality of sleep, measured by the Richards-Campbell Sleep Questionnaire (RCSQ). Secondary outcome measures were use of sleep-inducing medication, delirium treatment medication, and patient-perceived nocturnal noise. A significant difference in slope in the percentage of delirium was observed between the pre- and post-intervention periods (-3.7% per time period, p=0.02). Quality of sleep was unaffected (0.3 per time period, p=0.85). The post-intervention group used significantly less sleep-inducing medication (psound-reduction protocol. However, reported sleep quality did not improve. Copyright © 2017. Published by Elsevier Ltd.

A combined teamwork training and work standardisation intervention in operating theatres: controlled interrupted time series study.

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Morgan, Lauren; Pickering, Sharon P; Hadi, Mohammed; Robertson, Eleanor; New, Steve; Griffin, Damian; Collins, Gary; Rivero-Arias, Oliver; Catchpole, Ken; McCulloch, Peter

2015-02-01

Teamwork training and system standardisation have both been proposed to reduce error and harm in surgery. Since the approaches differ markedly, there is potential for synergy between them. Controlled interrupted time series with a 3 month intervention and observation phases before and after. Operating theatres conducting elective orthopaedic surgery in a single hospital system (UK Hospital Trust). Teamwork training based on crew resource management plus training and follow-up support in developing standardised operating procedures. Focus of subsequent standardisation efforts decided by theatre staff. Paired observers watched whole procedures together. We assessed non-technical skills using NOTECHS II, technical performance using glitch rate and compliance with WHO checklist using a simple quality tool. We measured complication and readmission rates and hospital stay using hospital administrative records. Before/after change was compared in the active and control groups using two-way ANOVA and regression models. 1121 patients were operated on before and 1100 after intervention. 44 operations were observed before and 50 afterwards. Non-technical skills (p=0.002) and WHO compliance (pteamwork and system improvement causes marked improvements in team behaviour and WHO performance, but not technical performance or outcome. These findings are consistent with the synergistic hypothesis, but larger controlled studies with a strong implementation strategy are required to test potential outcome effects. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Mental health impacts of flooding: a controlled interrupted time series analysis of prescribing data in England.

Science.gov (United States)

Milojevic, Ai; Armstrong, Ben; Wilkinson, Paul

2017-10-01

There is emerging evidence that people affected by flooding suffer adverse impacts on their mental well-being, mostly based on self-reports. We examined prescription records for drugs used in the management of common mental disorder among primary care practices located in the vicinity of recent large flood events in England, 2011-2014. A controlled interrupted time series analysis was conducted of the number of prescribing items for antidepressant drugs in the year before and after the flood onset. Pre-post changes were compared by distance of the practice from the inundated boundaries among 930 practices located within 10 km of a flood. After control for deprivation and population density, there was an increase of 0.59% (95% CI 0.24 to 0.94) prescriptions in the postflood year among practices located within 1 km of a flood over and above the change observed in the furthest distance band. The increase was greater in more deprived areas. This study suggests an increase in prescribed antidepressant drugs in the year after flooding in primary care practices close to recent major floods in England. The degree to which the increase is actually concentrated in those flooded can only be determined by more detailed linkage studies. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The impact of an electronic health record on nurse sensitive patient outcomes: an interrupted time series analysis.

Science.gov (United States)

Dowding, Dawn W; Turley, Marianne; Garrido, Terhilda

2012-01-01

To evaluate the impact of electronic health record (EHR) implementation on nursing care processes and outcomes. Interrupted time series analysis, 2003-2009. A large US not-for-profit integrated health care organization. 29 hospitals in Northern and Southern California. An integrated EHR including computerized physician order entry, nursing documentation, risk assessment tools, and documentation tools. Percentage of patients with completed risk assessments for hospital acquired pressure ulcers (HAPUs) and falls (process measures) and rates of HAPU and falls (outcome measures). EHR implementation was significantly associated with an increase in documentation rates for HAPU risk (coefficient 2.21, 95% CI 0.67 to 3.75); the increase for fall risk was not statistically significant (0.36; -3.58 to 4.30). EHR implementation was associated with a 13% decrease in HAPU rates (coefficient -0.76, 95% CI -1.37 to -0.16) but no decrease in fall rates (-0.091; -0.29 to 0.11). Irrespective of EHR implementation, HAPU rates decreased significantly over time (-0.16; -0.20 to -0.13), while fall rates did not (0.0052; -0.01 to 0.02). Hospital region was a significant predictor of variation for both HAPU (0.72; 0.30 to 1.14) and fall rates (0.57; 0.41 to 0.72). The introduction of an integrated EHR was associated with a reduction in the number of HAPUs but not in patient fall rates. Other factors, such as changes over time and hospital region, were also associated with variation in outcomes. The findings suggest that EHR impact on nursing care processes and outcomes is dependent on a number of factors that should be further explored.

The effect of interruptions and prolonged treatment time in radiotherapy for nasopharyngeal carcinoma

International Nuclear Information System (INIS)

Kwong, Dora L.W.; Sham, Jonathan S.T.; Chua, Daniel T.T.; Choy, Damon T.K.; Au, Gordon K.H.; Wu, P.M.

1997-01-01

Purpose: The effect of interruptions and prolonged overall treatment time in radiotherapy for nasopharyngeal carcinoma and the significance of timing of interruption was investigated. Methods and Materials: Treatment records of 229 patients treated with continuous course (CC) and 567 patients treated with split course (SC) radiotherapy for nonmetastatic NPC were reviewed. Overall treatment time without inclusion of time for boost was calculated. Treatment that extended 1 week beyond scheduled time was considered prolonged. Outcome in patients who completed treatment 'per schedule' were compared with those who had 'prolonged' treatment. Because of known patient selection bias between CC and SC, patients on the two schedules were analyzed separately. Multivariate analysis was performed for patients on SC. Total number of days of interruption, age, sex, T and N stage, and the use of boost were tested for the whole SC group. Analysis on the effect of timing of interruption was performed in a subgroup of 223 patients on SC who had a single unplanned interruption. Timing of interruption, either before or after the fourth week for the unplanned interruption, was tested in addition to the other variables in multivariate analysis for this subgroup of SC. Results: Twenty-seven (11.8%) patients on CC and 96 (16.9%) patients on SC had prolonged treatment. Patients on SC who had prolonged treatment had significantly poorer loco-regional control rate and disease free survival when compared with those who completed radiotherapy per schedule (p = 0.0063 and 0.001, respectively, with adjustment for stage). For CC, the effect of prolonged treatment on outcome was not significant. The small number of events for patients on CC probably account for the insignificant finding. The number of days of interruption was confirmed as prognostic factor, independent of T and N stages, for loco-regional control and disease-free survival in multivariate analysis for SC. The hazard rate for loco

Evaluation of a clinical decision support tool for osteoporosis disease management: protocol for an interrupted time series design.

Science.gov (United States)

Kastner, Monika; Sawka, Anna; Thorpe, Kevin; Chignel, Mark; Marquez, Christine; Newton, David; Straus, Sharon E

2011-07-22

Osteoporosis affects over 200 million people worldwide at a high cost to healthcare systems. Although guidelines on assessing and managing osteoporosis are available, many patients are not receiving appropriate diagnostic testing or treatment. Findings from a systematic review of osteoporosis interventions, a series of mixed-methods studies, and advice from experts in osteoporosis and human-factors engineering were used collectively to develop a multicomponent tool (targeted to family physicians and patients at risk for osteoporosis) that may support clinical decision making in osteoporosis disease management at the point of care. A three-phased approach will be used to evaluate the osteoporosis tool. In phase 1, the tool will be implemented in three family practices. It will involve ensuring optimal functioning of the tool while minimizing disruption to usual practice. In phase 2, the tool will be pilot tested in a quasi-experimental interrupted time series (ITS) design to determine if it can improve osteoporosis disease management at the point of care. Phase 3 will involve conducting a qualitative postintervention follow-up study to better understand participants' experiences and perceived utility of the tool and readiness to adopt the tool at the point of care. The osteoporosis tool has the potential to make several contributions to the development and evaluation of complex, chronic disease interventions, such as the inclusion of an implementation strategy prior to conducting an evaluation study. Anticipated benefits of the tool may be to increase awareness for patients about osteoporosis and its associated risks and provide an opportunity to discuss a management plan with their physician, which may all facilitate patient self-management.

The impact of public transportation strikes on use of a bicycle share program in London: interrupted time series design.

Science.gov (United States)

Fuller, Daniel; Sahlqvist, Shannon; Cummins, Steven; Ogilvie, David

2012-01-01

To investigate the immediate and sustained effects of two London Underground strikes on use of a public bicycle share program. An interrupted time series design was used to examine the impact of two 24 hour strikes on the total number of trips per day and mean trip duration per day on the London public bicycle share program. The strikes occurred on September 6th and October 4th 2010 and limited service on the London Underground. The mean total number of trips per day over the whole study period was 14,699 (SD=5390) while the mean trip duration was 18.5 minutes (SD=3.7). Significant increases in daily trip count were observed following strike 1 (3864: 95% CI 125 to 7604) and strike 2 (11,293: 95% CI 5169 to 17,416). Events that greatly constrain the primary motorised mode of transportation for a population may have unintended short-term effects on travel behaviour. These findings suggest that limiting transportation options may have the potential to increase population levels of physical activity by promoting the use of cycling. Copyright © 2011 Elsevier Inc. All rights reserved.

The impact of "Option B" on HIV transmission from mother to child in Rwanda: An interrupted time series analysis.

Science.gov (United States)

Abimpaye, Monique; Kirk, Catherine M; Iyer, Hari S; Gupta, Neil; Remera, Eric; Mugwaneza, Placidie; Law, Michael R

2018-01-01

Nearly a quarter of a million children have acquired HIV, prompting the implementation of new protocols-Option B and B+-for treating HIV+ pregnant women. While efficacy has been demonstrated in randomized trials, there is limited real-world evidence on the impact of these changes. Using longitudinal, routinely collected data we assessed the impact of the adoption of WHO Option B in Rwanda on mother to infant transmission. We used interrupted time series analysis to evaluate the impact of Option B on mother-to-child HIV transmission in Rwanda. Our primary outcome was the proportion of HIV tests in infants with positive results at six weeks of age. We included data for 20 months before and 22 months after the 2010 policy change. Of the 15,830 HIV tests conducted during our study period, 392 tested positive. We found a significant decrease in both the level (-2.08 positive tests per 100 tests conducted, 95% CI: -2.71 to -1.45, p Option B in Rwanda contributed to an immediate decrease in the rate of HIV transmission from mother to child. This suggests other countries may benefit from adopting these WHO guidelines.

Power Supply Interruption Costs: Models and Methods Incorporating Time Dependent Patterns

International Nuclear Information System (INIS)

Kjoelle, G.H.

1996-12-01

This doctoral thesis develops models and methods for estimation of annual interruption costs for delivery points, emphasizing the handling of time dependent patterns and uncertainties in the variables determining the annual costs. It presents an analytical method for calculation of annual expected interruption costs for delivery points in radial systems, based on a radial reliability model, with time dependent variables. And a similar method for meshed systems, based on a list of outage events, assuming that these events are found in advance from load flow and contingency analyses. A Monte Carlo simulation model is given which handles both time variations and stochastic variations in the input variables and is based on the same list of outage events. This general procedure for radial and meshed systems provides expectation values and probability distributions for interruption costs from delivery points. There is also a procedure for handling uncertainties in input variables by a fuzzy description, giving annual interruption costs as a fuzzy membership function. The methods are developed for practical applications in radial and meshed systems, based on available data from failure statistics, load registrations and customer surveys. Traditional reliability indices such as annual interruption time, power- and energy not supplied, are calculated as by-products. The methods are presented as algorithms and/or procedures which are available as prototypes. 97 refs., 114 figs., 62 tabs

Power Supply Interruption Costs: Models and Methods Incorporating Time Dependent Patterns

Energy Technology Data Exchange (ETDEWEB)

Kjoelle, G.H.

1996-12-01

This doctoral thesis develops models and methods for estimation of annual interruption costs for delivery points, emphasizing the handling of time dependent patterns and uncertainties in the variables determining the annual costs. It presents an analytical method for calculation of annual expected interruption costs for delivery points in radial systems, based on a radial reliability model, with time dependent variables. And a similar method for meshed systems, based on a list of outage events, assuming that these events are found in advance from load flow and contingency analyses. A Monte Carlo simulation model is given which handles both time variations and stochastic variations in the input variables and is based on the same list of outage events. This general procedure for radial and meshed systems provides expectation values and probability distributions for interruption costs from delivery points. There is also a procedure for handling uncertainties in input variables by a fuzzy description, giving annual interruption costs as a fuzzy membership function. The methods are developed for practical applications in radial and meshed systems, based on available data from failure statistics, load registrations and customer surveys. Traditional reliability indices such as annual interruption time, power- and energy not supplied, are calculated as by-products. The methods are presented as algorithms and/or procedures which are available as prototypes. 97 refs., 114 figs., 62 tabs.

An electronic trigger tool to optimise intravenous to oral antibiotic switch: a controlled, interrupted time series study

Directory of Open Access Journals (Sweden)

Marvin A. H. Berrevoets

2017-08-01

Full Text Available Abstract Background Timely switch from intravenous (iv antibiotics to oral therapy is a key component of antimicrobial stewardship programs in order to improve patient safety, promote early discharge and reduce costs. We have introduced a time-efficient and easily implementable intervention that relies on a computerized trigger tool, which identifies patients who are candidates for an iv to oral antibiotic switch. Methods The intervention was introduced on all internal medicine wards in a teaching hospital. Patients were automatically identified by an electronic trigger tool when parenteral antibiotics were used for >48 h and clinical or pharmacological data did not preclude switch therapy. A weekly educational session was introduced to alert the physicians on the intervention wards. The intervention wards were compared with control wards, which included all other hospital wards. An interrupted time-series analysis was performed to compare the pre-intervention period with the post-intervention period using ‘% of i.v. prescriptions >72 h’ and ‘median duration of iv therapy per prescription’ as outcomes. We performed a detailed prospective evaluation on a subset of 244 prescriptions to evaluate the efficacy and appropriateness of the intervention. Results The number of intravenous prescriptions longer than 72 h was reduced by 19% in the intervention group (n = 1519 (p < 0.01 and the median duration of iv antibiotics was reduced with 0.8 days (p = <0.05. Compared to the control group (n = 4366 the intervention was responsible for an additional decrease of 13% (p < 0.05 in prolonged prescriptions. The detailed prospective evaluation of a subgroup of patients showed that adherence to the electronic reminder was 72%. Conclusions An electronic trigger tool combined with a weekly educational session was effective in reducing the duration of intravenous antimicrobial therapy.

Total and cause-specific mortality before and after the onset of the Greek economic crisis: an interrupted time-series analysis.

Science.gov (United States)

Laliotis, Ioannis; Ioannidis, John P A; Stavropoulou, Charitini

2016-12-01

Greece was one of the countries hit the hardest by the 2008 financial crisis in Europe. Yet, evidence on the effect of the crisis on total and cause-specific mortality remains unclear. We explored whether the economic crisis affected the trend of overall and cause-specific mortality rates. We used regional panel data from the Hellenic Statistical Authority to assess mortality trends by age, sex, region, and cause in Greece between January, 2001, and December, 2013. We used Eurostat data to calculate monthly age-standardised mortality rates per 100 000 inhabitants for each region. Data were divided into two subperiods: before the crisis (January, 2001, to August, 2008) and after the onset of the crisis (September, 2008, to December, 2013). We tested for changes in the slope of mortality by doing an interrupted time-series analysis. Overall mortality continued to decline after the onset of the financial crisis (-0·065, 95% CI -0·080 to -0·049), but at a slower pace than before the crisis (-0·13, -0·15 to -0·10; trend difference 0·062, 95% CI 0·041 to 0·083; pperiod after the onset of the crisis with extrapolated values based on the period before the crisis, we estimate that an extra 242 deaths per month occurred after the onset of the crisis. Mortality trends have been interrupted after the onset of compared with before the crisis, but changes vary by age, sex, and cause of death. The increase in deaths due to adverse events during medical treatment might reflect the effects of deterioration in quality of care during economic recessions. None. Copyright © 2016 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.

Sequential hand hygiene promotion contributes to a reduced nosocomial bloodstream infection rate among very low-birth weight infants: an interrupted time series over a 10-year period.

Science.gov (United States)

Helder, Onno K; Brug, Johannes; van Goudoever, Johannes B; Looman, Caspar W N; Reiss, Irwin K M; Kornelisse, René F

2014-07-01

Sustained high compliance with hand hygiene (HH) is needed to reduce nosocomial bloodstream infections (NBSIs). However, over time, a wash out effect often occurs. We studied the long-term effect of sequential HH-promoting interventions. An observational study with an interrupted time series analysis of the occurrence of NBSI was performed in very low-birth weight (VLBW) infants. Interventions consisted of an education program, gain-framed screen saver messages, and an infection prevention week with an introduction on consistent glove use. A total of 1,964 VLBW infants admitted between January 1, 2002, and December 31, 2011, were studied. The proportion of infants with ≥1 NBSI decreased from 47.6%-21.2% (P Infection Control and Epidemiology, Inc. Published by Mosby, Inc. All rights reserved.

Impact of a COPD discharge care bundle on readmissions following admission with acute exacerbation: interrupted time series analysis.

Directory of Open Access Journals (Sweden)

Anthony A Laverty

Full Text Available We evaluated the impact of a COPD discharge care bundle on readmission rates following hospitalisation with an acute exacerbation.Interrupted time series analysis, comparing readmission rates for COPD exacerbations at nine trusts that introduced the bundle, to two comparison groups; (1 other NHS trusts in London and (2 all other NHS trusts in England. Care bundles were implemented at different times for different NHS trusts, ranging from October 2009 to April 2011.Nine NHS acute trusts in the London, England.Patients aged 45 years and older admitted to an NHS acute hospital in England for acute exacerbation of COPD. Data come from Hospital Episode Statistics, April 2002 to March 2012.Annual trend readmission rates (and in total bed days within 7, 28 and 90 days, before and after implementation.In hospitals introducing the bundle readmission rates were rising before implementation and falling afterwards (e.g. readmissions within 28 days +2.13% per annum (pa pre and -5.32% pa post (p for difference in trends = 0.012. Following implementation, readmission rates within 7 and 28 day were falling faster than among other trusts in London, although this was not statistically significant (e.g. readmissions within 28 days -4.6% pa vs. -3.2% pa, p = 0.44. Comparisons with a national control group were similar.The COPD discharge care bundle appeared to be associated with a reduction in readmission rate among hospitals using it. The significance of this is unclear because of changes to background trends in London and nationally.

Impact of the zero-markup drug policy on hospitalisation expenditure in western rural China: an interrupted time series analysis.

Science.gov (United States)

Yang, Caijun; Shen, Qian; Cai, Wenfang; Zhu, Wenwen; Li, Zongjie; Wu, Lina; Fang, Yu

2017-02-01

To assess the long-term effects of the introduction of China's zero-markup drug policy on hospitalisation expenditure and hospitalisation expenditures after reimbursement. An interrupted time series was used to evaluate the impact of the zero-markup drug policy on hospitalisation expenditure and hospitalisation expenditure after reimbursement at primary health institutions in Fufeng County of Shaanxi Province, western China. Two regression models were developed. Monthly average hospitalisation expenditure and monthly average hospitalisation expenditure after reimbursement in primary health institutions were analysed covering the period 2009 through to 2013. For the monthly average hospitalisation expenditure, the increasing trend was slowed down after the introduction of the zero-markup drug policy (coefficient = -16.49, P = 0.009). For the monthly average hospitalisation expenditure after reimbursement, the increasing trend was slowed down after the introduction of the zero-markup drug policy (coefficient = -10.84, P = 0.064), and a significant decrease in the intercept was noted after the second intervention of changes in reimbursement schemes of the new rural cooperative medical insurance (coefficient = -220.64, P markup drug policy in western China. However, hospitalisation expenditure and hospitalisation expenditure after reimbursement were still increasing. More effective policies are needed to prevent these costs from continuing to rise. © 2016 John Wiley & Sons Ltd.

Multifaceted academic detailing program to increase pharmacotherapy for alcohol use disorder: interrupted time series evaluation of effectiveness.

Science.gov (United States)

Harris, Alex H S; Bowe, Thomas; Hagedorn, Hildi; Nevedal, Andrea; Finlay, Andrea K; Gidwani, Risha; Rosen, Craig; Kay, Chad; Christopher, Melissa

2016-09-15

Active consideration of effective medications to treat alcohol use disorder (AUD) is a consensus standard of care, yet knowledge and use of these medications are very low across diverse settings. This study evaluated the overall effectiveness a multifaceted academic detailing program to address this persistent quality problem in the US Veterans Health Administration (VHA), as well as the context and process factors that explained variation in effectiveness across sites. An interrupted time series design, analyzed with mixed-effects segmented logistic regression, was used to evaluate changes in level and rate of change in the monthly percent of patients with a clinically documented AUD who received naltrexone, acamprosate, disulfiram, or topiramate. Using data from a 20 month post-implementation period, intervention sites (n = 37) were compared to their own 16 month pre-implementation performance and separately to the rest of VHA. From immediately pre-intervention to the end of the observation period, the percent of patients in the intervention sites with AUD who received medication increased over 3.4 % in absolute terms and 68 % in relative terms (i.e., 4.9-8.3 %). This change was significant compared to the pre-implementation period in the intervention sites and secular trends in control sites. Sites with lower pre-implementation adoption, more person hours of detailing, but fewer people detailed, had larger immediate increases in medication receipt after implementation. The average number of detailing encounters per person was associated with steeper increases in slope over time. This study found empirical support for a multifaceted quality improvement strategy aimed at increasing access to and utilization of pharmacotherapy for AUD. Future studies should focus on determining how to enhance the programs effects, especially in non-responsive locations.

Effectiveness of employer financial incentives in reducing time to report worker injury: an interrupted time series study of two Australian workers' compensation jurisdictions.

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Lane, Tyler J; Gray, Shannon; Hassani-Mahmooei, Behrooz; Collie, Alex

2018-01-05

Early intervention following occupational injury can improve health outcomes and reduce the duration and cost of workers' compensation claims. Financial early reporting incentives (ERIs) for employers may shorten the time between injury and access to compensation benefits and services. We examined ERI effect on time spent in the claim lodgement process in two Australian states: South Australia (SA), which introduced them in January 2009, and Tasmania (TAS), which introduced them in July 2010. Using administrative records of 1.47 million claims lodged between July 2006 and June 2012, we conducted an interrupted time series study of ERI impact on monthly median days in the claim lodgement process. Time periods included claim reporting, insurer decision, and total time. The 18-month gap in implementation between the states allowed for a multiple baseline design. In SA, we analysed periods within claim reporting: worker and employer reporting times (similar data were not available in TAS). To account for external threats to validity, we examined impact in reference to a comparator of other Australian workers' compensation jurisdictions. Total time in the process did not immediately change, though trend significantly decreased in both jurisdictions (SA: -0.36 days per month, 95% CI -0.63 to -0.09; TAS: 0.35, -0.50 to -0.20). Claim reporting time also decreased in both (SA: -1.6 days, -2.4 to -0.8; TAS: -5.4, -7.4 to -3.3). In TAS, there was a significant increase in insurer decision time (4.6, 3.9 to 5.4) and a similar but non-significant pattern in SA. In SA, worker reporting time significantly decreased (-4.7, -5.8 to -3.5), but employer reporting time did not (-0.3, -0.8 to 0.2). The results suggest that ERIs reduced claim lodgement time and, in the long-term, reduced total time in the claim lodgement process. However, only worker reporting time significantly decreased in SA, indicating that ERIs may not have shortened the process through the intended target of

A robust interrupted time series model for analyzing complex health care intervention data

KAUST Repository

Cruz, Maricela

2017-08-29

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be

A robust interrupted time series model for analyzing complex health care intervention data

KAUST Repository

Cruz, Maricela; Bender, Miriam; Ombao, Hernando

2017-01-01

Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be

The effects of pay for performance on disparities in stroke, hypertension, and coronary heart disease management: interrupted time series study.

Science.gov (United States)

Lee, John Tayu; Netuveli, Gopalakrishnan; Majeed, Azeem; Millett, Christopher

2011-01-01

The Quality and Outcomes Framework (QOF), a major pay-for-performance programme, was introduced into United Kingdom primary care in April 2004. The impact of this programme on disparities in health care remains unclear. This study examines the following questions: has this pay for performance programme improved the quality of care for coronary heart disease, stroke and hypertension in white, black and south Asian patients? Has this programme reduced disparities in the quality of care between these ethnic groups? Did general practices with different baseline performance respond differently to this programme? Retrospective cohort study of patients registered with family practices in Wandsworth, London during 2007. Segmented regression analysis of interrupted time series was used to take into account the previous time trend. Primary outcome measures were mean systolic and diastolic blood pressure, and cholesterol levels. Our findings suggest that the implementation of QOF resulted in significant short term improvements in blood pressure control. The magnitude of benefit varied between ethnic groups with a statistically significant short term reduction in systolic BP in white and black but not in south Asian patients with hypertension. Disparities in risk factor control were attenuated only on few measures and largely remained intact at the end of the study period. Pay for performance programmes such as the QOF in the UK should set challenging but achievable targets. Specific targets aimed at reducing ethnic disparities in health care may also be needed.

Impact of pharmaceutical policy interventions on utilization of antipsychotic medicines in Finland and Portugal in times of economic recession: interrupted time series analyses.

Science.gov (United States)

Leopold, Christine; Zhang, Fang; Mantel-Teeuwisse, Aukje K; Vogler, Sabine; Valkova, Silvia; Ross-Degnan, Dennis; Wagner, Anita K

2014-07-25

To analyze the impacts of pharmaceutical sector policies implemented to contain country spending during the economic recession--a reference price system in Finland and a mix of policies including changes in reimbursement rates, a generic promotion campaign and discounts granted to the public payer in Portugal - on utilization of, as a proxy for access to, antipsychotic medicines. We obtained monthly IMS Health sales data in standard units of antipsychotic medicines in Portugal and Finland for the period January 2007 to December 2011. We used an interrupted time series design to estimate changes in overall use and generic market shares by comparing pre-policy and post-policy levels and trends. Both countries' policy approaches were associated with slight, likely unintended, decreases in overall use of antipsychotic medicines and with increases in generic market shares of major antipsychotic products. In Finland, quetiapine and risperidone generic market shares increased substantially (estimates one year post-policy compared to before, quetiapine: 6.80% [3.92%, 9.68%]; risperidone: 11.13% [6.79%, 15.48%]. The policy interventions in Portugal resulted in a substantially increased generic market share for amisulpride (estimate one year post-policy compared to before: 22.95% [21.01%, 24.90%]; generic risperidone already dominated the market prior to the policy interventions. Different policy approaches to contain pharmaceutical expenditures in times of the economic recession in Finland and Portugal had intended--increased use of generics--and likely unintended--slightly decreased overall sales, possibly consistent with decreased access to needed medicines--impacts. These findings highlight the importance of monitoring and evaluating the effects of pharmaceutical policy interventions on use of medicines and health outcomes.

Real time interrupt handling using FORTRAN IV plus under RSX-11M

International Nuclear Information System (INIS)

Schultz, D.E.

1981-01-01

A real-time data acquisition application for a linear accelerator is described. The important programming features of this application are use of connect to interrupt, a shared library, map to I/O page, and a shared data area. How you can provide rapid interrupt handling using these tools from FORTRAN IV PLUS is explained

The effect of the late 2000s financial crisis on suicides in Spain: an interrupted time-series analysis.

Science.gov (United States)

Lopez Bernal, James A; Gasparrini, Antonio; Artundo, Carlos M; McKee, Martin

2013-10-01

The current financial crisis is having a major impact on European economies, especially that of Spain. Past evidence suggests that adverse macro-economic conditions exacerbate mental illness, but evidence from the current crisis is limited. This study analyses the association between the financial crisis and suicide rates in Spain. An interrupted time-series analysis of national suicides data between 2005 and 2010 was used to establish whether there has been any deviation in the underlying trend in suicide rates associated with the financial crisis. Segmented regression with a seasonally adjusted quasi-Poisson model was used for the analysis. Stratified analyses were performed to establish whether the effect of the crisis on suicides varied by region, sex and age group. The mean monthly suicide rate in Spain during the study period was 0.61 per 100 000 with an underlying trend of a 0.3% decrease per month. We found an 8.0% increase in the suicide rate above this underlying trend since the financial crisis (95% CI: 1.009-1.156; P = 0.03); this was robust to sensitivity analysis. A control analysis showed no change in deaths from accidental falls associated with the crisis. Stratified analyses suggested that the association between the crisis and suicide rates is greatest in the Mediterranean and Northern areas, in males and amongst those of working age. The financial crisis in Spain has been associated with a relative increase in suicides. Males and those of working age may be at particular risk of suicide associated with the crisis and may benefit from targeted interventions.

Effect of a population-level performance dashboard intervention on maternal-newborn outcomes: an interrupted time series study.

Science.gov (United States)

Weiss, Deborah; Dunn, Sandra I; Sprague, Ann E; Fell, Deshayne B; Grimshaw, Jeremy M; Darling, Elizabeth; Graham, Ian D; Harrold, JoAnn; Smith, Graeme N; Peterson, Wendy E; Reszel, Jessica; Lanes, Andrea; Walker, Mark C; Taljaard, Monica

2018-06-01

To assess the effect of the Maternal Newborn Dashboard on six key clinical performance indicators in the province of Ontario, Canada. Interrupted time series using population-based data from the provincial birth registry covering a 3-year period before implementation of the Dashboard and 2.5 years after implementation (November 2009 through March 2015). All hospitals in the province of Ontario providing maternal-newborn care (n=94). A hospital-based online audit and feedback programme. Rates of the six performance indicators included in the Dashboard. 2.5 years after implementation, the audit and feedback programme was associated with statistically significant absolute decreases in the rates of episiotomy (decrease of 1.5 per 100 women, 95% CI 0.64 to 2.39), induction for postdates in women who were less than 41 weeks at delivery (decrease of 11.7 per 100 women, 95% CI 7.4 to 16.0), repeat caesarean delivery in low-risk women performed before 39 weeks (decrease of 10.4 per 100 women, 95% CI 9.3 to 11.5) and an absolute increase in the rate of appropriately timed group B streptococcus screening (increase of 2.8 per 100, 95% CI 2.2 to 3.5). The audit and feedback programme did not significantly affect the rates of unsatisfactory newborn screening blood samples or formula supplementation at discharge. No statistically significant effects were observed for the two internal control outcomes or the four external control indicators-in fact, two external control indicators (episiotomy and postdates induction) worsened relative to before implementation. An electronic audit and feedback programme implemented in maternal-newborn hospitals was associated with clinically relevant practice improvements at the provincial level in the majority of targeted indicators. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Prediction of Unmet Primary Care Needs for the Medically Vulnerable Post-Disaster: An Interrupted Time-Series Analysis of Health System Responses

Directory of Open Access Journals (Sweden)

Amy B. Martin

2012-09-01

Full Text Available Disasters serve as shocks and precipitate unanticipated disturbances to the health care system. Public health surveillance is generally focused on monitoring latent health and environmental exposure effects, rather than health system performance in response to these local shocks. The following intervention study sought to determine the long-term effects of the 2005 chlorine spill in Graniteville, South Carolina on primary care access for vulnerable populations. We used an interrupted time-series approach to model monthly visits for Ambulatory Care Sensitive Conditions, an indicator of unmet primary care need, to quantify the impact of the disaster on unmet primary care need in Medicaid beneficiaries. The results showed Medicaid beneficiaries in the directly impacted service area experienced improved access to primary care in the 24 months post-disaster. We provide evidence that a health system serving the medically underserved can prove resilient and display improved adaptive capacity under adverse circumstances (i.e., technological disasters to ensure access to primary care for vulnerable sub-groups. The results suggests a new application for ambulatory care sensitive conditions as a population-based metric to advance anecdotal evidence of secondary surge and evaluate pre- and post-health system surge capacity following a disaster.

Water supply interruptions and suspected cholera incidence: a time-series regression in the Democratic Republic of the Congo.

Science.gov (United States)

Jeandron, Aurélie; Saidi, Jaime Mufitini; Kapama, Alois; Burhole, Manu; Birembano, Freddy; Vandevelde, Thierry; Gasparrini, Antonio; Armstrong, Ben; Cairncross, Sandy; Ensink, Jeroen H J

2015-10-01

The eastern provinces of the Democratic Republic of the Congo have been identified as endemic areas for cholera transmission, and despite continuous control efforts, they continue to experience regular cholera outbreaks that occasionally spread to the rest of the country. In a region where access to improved water sources is particularly poor, the question of which improvements in water access should be prioritized to address cholera transmission remains unresolved. This study aimed at investigating the temporal association between water supply interruptions and Cholera Treatment Centre (CTC) admissions in a medium-sized town. Time-series patterns of daily incidence of suspected cholera cases admitted to the Cholera Treatment Centre in Uvira in South Kivu Province between 2009 and 2014 were examined in relation to the daily variations in volume of water supplied by the town water treatment plant. Quasi-poisson regression and distributed lag nonlinear models up to 12 d were used, adjusting for daily precipitation rates, day of the week, and seasonal variations. A total of 5,745 patients over 5 y of age with acute watery diarrhoea symptoms were admitted to the CTC over the study period of 1,946 d. Following a day without tap water supply, the suspected cholera incidence rate increased on average by 155% over the next 12 d, corresponding to a rate ratio of 2.55 (95% CI: 1.54-4.24), compared to the incidence experienced after a day with optimal production (defined as the 95th percentile-4,794 m3). Suspected cholera cases attributable to a suboptimal tap water supply reached 23.2% of total admissions (95% CI 11.4%-33.2%). Although generally reporting less admissions to the CTC, neighbourhoods with a higher consumption of tap water were more affected by water supply interruptions, with a rate ratio of 3.71 (95% CI: 1.91-7.20) and an attributable fraction of cases of 31.4% (95% CI: 17.3%-42.5%). The analysis did not suggest any association between levels of residual

The Impact of a Case of Ebola Virus Disease on Emergency Department Visits in Metropolitan Dallas-Fort Worth, TX, July, 2013-July, 2015: An Interrupted Time Series Analysis.

Science.gov (United States)

Molinari, Noelle-Angelique M; LeBlanc, Tanya Telfair; Stephens, William

2018-03-20

The first Ebola virus disease (EVD) case in the United States (US) was confirmed September 30, 2014 in a man 45 years old. This event created considerable media attention and there was fear of an EVD outbreak in the US. This study examined whether emergency department (ED) visits changed in metropolitan Dallas-Fort Worth--, Texas (DFW) after this EVD case was confirmed. Using Texas Health Services Region 2/3 syndromic surveillance data and focusing on DFW, interrupted time series analyses were conducted using segmented regression models with autoregressive errors for overall ED visits and rates of several chief complaints, including fever with gastrointestinal distress (FGI). Date of fatal case confirmation was the "event." Results indicated the event was highly significant for ED visits overall (Pcapacity as well as for public health messaging in the wake of a public health emergency.

Segmented regression analysis of interrupted time series data to assess outcomes of a South American road traffic alcohol policy change.

Science.gov (United States)

Nistal-Nuño, Beatriz

2017-09-01

In Chile, a new law introduced in March 2012 decreased the legal blood alcohol concentration (BAC) limit for driving while impaired from 1 to 0.8 g/l and the legal BAC limit for driving under the influence of alcohol from 0.5 to 0.3 g/l. The goal is to assess the impact of this new law on mortality and morbidity outcomes in Chile. A review of national databases in Chile was conducted from January 2003 to December 2014. Segmented regression analysis of interrupted time series was used for analyzing the data. In a series of multivariable linear regression models, the change in intercept and slope in the monthly incidence rate of traffic deaths and injuries and association with alcohol per 100,000 inhabitants was estimated from pre-intervention to postintervention, while controlling for secular changes. In nested regression models, potential confounding seasonal effects were accounted for. All analyses were performed at a two-sided significance level of 0.05. Immediate level drops in all the monthly rates were observed after the law from the end of the prelaw period in the majority of models and in all the de-seasonalized models, although statistical significance was reached only in the model for injures related to alcohol. After the law, the estimated monthly rate dropped abruptly by -0.869 for injuries related to alcohol and by -0.859 adjusting for seasonality (P < 0.001). Regarding the postlaw long-term trends, it was evidenced a steeper decreasing trend after the law in the models for deaths related to alcohol, although these differences were not statistically significant. A strong evidence of a reduction in traffic injuries related to alcohol was found following the law in Chile. Although insufficient evidence was found of a statistically significant effect for the beneficial effects seen on deaths and overall injuries, potential clinically important effects cannot be ruled out. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd

Interrupt Handlers in Java

DEFF Research Database (Denmark)

Korsholm, Stephan; Schoeberl, Martin; Ravn, Anders Peter

2008-01-01

An important part of implementing device drivers is to control the interrupt facilities of the hardware platform and to program interrupt handlers. Current methods for handling interrupts in Java use a server thread waiting for the VM to signal an interrupt occurrence. It means that the interrupt...... is handled at a later time, which has some disadvantages. We present constructs that allow interrupts to be handled directly and not at a later point decided by a scheduler. A desirable feature of our approach is that we do not require a native middleware layer but can handle interrupts entirely with Java...... code. We have implemented our approach using an interpreter and a Java processor, and give an example demonstrating its use....

PERFORMANCE COMPARISON OF USART COMMUNICATION BETWEEN REAL TIME OPERATING SYSTEM (RTOS AND NATIVE INTERRUPT

Directory of Open Access Journals (Sweden)

Novian Habibie

2016-02-01

Full Text Available Comunication between microcontrollers is one of the crucial point in embedded sytems. On the other hand, embedded system must be able to run many parallel task simultaneously. To handle this, we need a reliabe system that can do a multitasking without decreasing every task’s performance. The most widely used methods for multitasking in embedded systems are using Interrupt Service Routine (ISR or using Real Time Operating System (RTOS. This research compared perfomance of USART communication on system with RTOS to a system that use interrupt. Experiments run on two identical development board XMega A3BU-Xplained which used intenal sensor (light and temperature and used servo as external component. Perfomance comparison done by counting ping time (elapsing time to transmit data and get a reply as a mark that data has been received and compare it. This experiments divided into two scenarios: (1 system loaded with many tasks, (2 system loaded with few tasks. Result of the experiments show that communication will be faster if system only loaded with few tasks. System with RTOS has won from interrupt in case (1, but lose to interrupt in case (2.

A prospective interrupted time series study of interventions to improve the quality, rating, framing and structure of goal-setting in community-based brain injury rehabilitation.

Science.gov (United States)

Hassett, Leanne; Simpson, Grahame; Cotter, Rachel; Whiting, Diane; Hodgkinson, Adeline; Martin, Diane

2015-04-01

To investigate whether the introduction of an electronic goals system followed by staff training improved the quality, rating, framing and structure of goals written by a community-based brain injury rehabilitation team. Interrupted time series design. Two interventions were introduced six months apart. The first intervention comprised the introduction of an electronic goals system. The second intervention comprised a staff goal training workshop. An audit protocol was devised to evaluate the goals. A random selection of goal statements from the 12 months prior to the interventions (Time 1 baseline) were compared with all goal statements written after the introduction of the electronic goals system (Time 2) and staff training (Time 3). All goals were de-identified for client and time-period, and randomly ordered. A total of 745 goals (Time 1 n = 242; Time 2 n = 283; Time 3 n = 220) were evaluated. Compared with baseline, the introduction of the electronic goals system alone significantly increased goal rating, framing and structure (χ(2) tests 144.7, 18.9, 48.1, respectively, p goal quality, which was only a trend at Time 2, was statistically significant at Time 3 (χ(2) 15.0, p ≤ 001). The training also led to a further significant increase in the framing and structuring of goals over the electronic goals system (χ(2) 11.5, 12.5, respectively, p ≤ 0.001). An electronic goals system combined with staff training improved the quality, rating, framing and structure of goal statements. © The Author(s) 2014.

Changing use of surgical antibiotic prophylaxis in Thika Hospital, Kenya: a quality improvement intervention with an interrupted time series design.

Directory of Open Access Journals (Sweden)

Alexander M Aiken

Full Text Available In low-income countries, Surgical Site Infection (SSI is a common form of hospital-acquired infection. Antibiotic prophylaxis is an effective method of preventing these infections, if given immediately before the start of surgery. Although several studies in Africa have compared pre-operative versus post-operative prophylaxis, there are no studies describing the implementation of policies to improve prescribing of surgical antibiotic prophylaxis in African hospitals.We conducted SSI surveillance at a typical Government hospital in Kenya over a 16 month period between August 2010 and December 2011, using standard definitions of SSI and the extent of contamination of surgical wounds. As an intervention, we developed a hospital policy that advised pre-operative antibiotic prophylaxis and discouraged extended post-operative antibiotics use. We measured process, outcome and balancing effects of this intervention in using an interrupted time series design.From a starting point of near-exclusive post-operative antibiotic use, after policy introduction in February 2011 there was rapid adoption of the use of pre-operative antibiotic prophylaxis (60% of operations at 1 week; 98% at 6 weeks and a substantial decrease in the use of post-operative antibiotics (40% of operations at 1 week; 10% at 6 weeks in Clean and Clean-Contaminated surgery. There was no immediate step-change in risk of SSI, but overall, there appeared to be a moderate reduction in the risk of superficial SSI across all levels of wound contamination. There were marked reductions in the costs associated with antibiotic use, the number of intravenous injections performed and nursing time spent administering these.Implementation of a locally developed policy regarding surgical antibiotic prophylaxis is an achievable quality improvement target for hospitals in low-income countries, and can lead to substantial benefits for individual patients and the institution.

Changing use of surgical antibiotic prophylaxis in Thika Hospital, Kenya: a quality improvement intervention with an interrupted time series design.

Science.gov (United States)

Aiken, Alexander M; Wanyoro, Anthony K; Mwangi, Jonah; Juma, Francis; Mugoya, Isaac K; Scott, J Anthony G

2013-01-01

In low-income countries, Surgical Site Infection (SSI) is a common form of hospital-acquired infection. Antibiotic prophylaxis is an effective method of preventing these infections, if given immediately before the start of surgery. Although several studies in Africa have compared pre-operative versus post-operative prophylaxis, there are no studies describing the implementation of policies to improve prescribing of surgical antibiotic prophylaxis in African hospitals. We conducted SSI surveillance at a typical Government hospital in Kenya over a 16 month period between August 2010 and December 2011, using standard definitions of SSI and the extent of contamination of surgical wounds. As an intervention, we developed a hospital policy that advised pre-operative antibiotic prophylaxis and discouraged extended post-operative antibiotics use. We measured process, outcome and balancing effects of this intervention in using an interrupted time series design. From a starting point of near-exclusive post-operative antibiotic use, after policy introduction in February 2011 there was rapid adoption of the use of pre-operative antibiotic prophylaxis (60% of operations at 1 week; 98% at 6 weeks) and a substantial decrease in the use of post-operative antibiotics (40% of operations at 1 week; 10% at 6 weeks) in Clean and Clean-Contaminated surgery. There was no immediate step-change in risk of SSI, but overall, there appeared to be a moderate reduction in the risk of superficial SSI across all levels of wound contamination. There were marked reductions in the costs associated with antibiotic use, the number of intravenous injections performed and nursing time spent administering these. Implementation of a locally developed policy regarding surgical antibiotic prophylaxis is an achievable quality improvement target for hospitals in low-income countries, and can lead to substantial benefits for individual patients and the institution.

BED-time charts and their application to the problems of interruptions in external beam radiotherapy treatments

International Nuclear Information System (INIS)

Sinclair, Judith A.; Oates, Jason P.; Dale, Roger G.

1999-01-01

Purpose: The use of radiobiological modelling to examine the likely consequences of interruptions to radiotherapy schedules and to assess various compensatory measures. Methods and Materials: An effect-time graphical display, the BED-time chart, has been developed using the linear-quadratic (LQ) model. This is used to examine the effects on tumour and normal tissues of treatment interruption scenarios representative of clinical situations. The mathematical criteria governing successful salvage have also been drafted and applied to typical situations. Results: The successful salvage of an interrupted treatment is dependent on a number of interacting factors and the method presented here can be used to examine the trade-offs that exist. Although the mathematics may be complex, it is shown that the dilemmas posed by an interrupted treatment may be more easily appreciated with reference to BED-time charts. These may therefore have a useful role as a teaching aid for portraying a wider variety of radiotherapy problems and also in the documentation of interruptions to treatment and the measures taken to compensate for them. Conclusions: Interruptions to radiotherapy regimes are undesirable and compensatory measures need to be initiated as soon as possible after the gap, with a view to completing the amended treatment within the originally prescribed treatment time. Adequate compensation is particularly difficult for long gaps and gaps which occur towards the end of the scheduled treatment. Modelling exercises can help establish guidelines on the available windows of opportunity

Water Supply Interruptions and Suspected Cholera Incidence: A Time-Series Regression in the Democratic Republic of the Congo

Science.gov (United States)

Jeandron, Aurélie; Saidi, Jaime Mufitini; Kapama, Alois; Burhole, Manu; Birembano, Freddy; Vandevelde, Thierry; Gasparrini, Antonio; Armstrong, Ben; Cairncross, Sandy; Ensink, Jeroen H. J.

2015-01-01

Background The eastern provinces of the Democratic Republic of the Congo have been identified as endemic areas for cholera transmission, and despite continuous control efforts, they continue to experience regular cholera outbreaks that occasionally spread to the rest of the country. In a region where access to improved water sources is particularly poor, the question of which improvements in water access should be prioritized to address cholera transmission remains unresolved. This study aimed at investigating the temporal association between water supply interruptions and Cholera Treatment Centre (CTC) admissions in a medium-sized town. Methods and Findings Time-series patterns of daily incidence of suspected cholera cases admitted to the Cholera Treatment Centre in Uvira in South Kivu Province between 2009 and 2014 were examined in relation to the daily variations in volume of water supplied by the town water treatment plant. Quasi-poisson regression and distributed lag nonlinear models up to 12 d were used, adjusting for daily precipitation rates, day of the week, and seasonal variations. A total of 5,745 patients over 5 y of age with acute watery diarrhoea symptoms were admitted to the CTC over the study period of 1,946 d. Following a day without tap water supply, the suspected cholera incidence rate increased on average by 155% over the next 12 d, corresponding to a rate ratio of 2.55 (95% CI: 1.54–4.24), compared to the incidence experienced after a day with optimal production (defined as the 95th percentile—4,794 m3). Suspected cholera cases attributable to a suboptimal tap water supply reached 23.2% of total admissions (95% CI 11.4%–33.2%). Although generally reporting less admissions to the CTC, neighbourhoods with a higher consumption of tap water were more affected by water supply interruptions, with a rate ratio of 3.71 (95% CI: 1.91–7.20) and an attributable fraction of cases of 31.4% (95% CI: 17.3%–42.5%). The analysis did not suggest any

Trends in the utilization of dental outpatient services affected by the expansion of health care benefits in South Korea to include scaling: a 6-year interrupted time-series study.

Science.gov (United States)

Park, Hee-Jung; Lee, Jun Hyup; Park, Sujin; Kim, Tae-Il

2018-02-01

This study utilized a strong quasi-experimental design to test the hypothesis that the implementation of a policy to expand dental care services resulted in an increase in the usage of dental outpatient services. A total of 45,650,000 subjects with diagnoses of gingivitis or advanced periodontitis who received dental scaling were selected and examined, utilizing National Health Insurance claims data from July 2010 through November 2015. We performed a segmented regression analysis of the interrupted time-series to analyze the time-series trend in dental costs before and after the policy implementation, and assessed immediate changes in dental costs. After the policy change was implemented, a statistically significant 18% increase occurred in the observed total dental cost per patient, after adjustment for age, sex, and residence area. In addition, the dental costs of outpatient gingivitis treatment increased immediately by almost 47%, compared with a 15% increase in treatment costs for advanced periodontitis outpatients. This policy effect appears to be sustainable. The introduction of the new policy positively impacted the immediate and long-term outpatient utilization of dental scaling treatment in South Korea. While the policy was intended to entice patients to prevent periodontal disease, thus benefiting the insurance system, our results showed that the policy also increased treatment accessibility for potential periodontal disease patients and may improve long-term periodontal health in the South Korean population.

Trends in the utilization of dental outpatient services affected by the expansion of health care benefits in South Korea to include scaling: a 6-year interrupted time-series study

Science.gov (United States)

2018-01-01

Purpose This study utilized a strong quasi-experimental design to test the hypothesis that the implementation of a policy to expand dental care services resulted in an increase in the usage of dental outpatient services. Methods A total of 45,650,000 subjects with diagnoses of gingivitis or advanced periodontitis who received dental scaling were selected and examined, utilizing National Health Insurance claims data from July 2010 through November 2015. We performed a segmented regression analysis of the interrupted time-series to analyze the time-series trend in dental costs before and after the policy implementation, and assessed immediate changes in dental costs. Results After the policy change was implemented, a statistically significant 18% increase occurred in the observed total dental cost per patient, after adjustment for age, sex, and residence area. In addition, the dental costs of outpatient gingivitis treatment increased immediately by almost 47%, compared with a 15% increase in treatment costs for advanced periodontitis outpatients. This policy effect appears to be sustainable. Conclusions The introduction of the new policy positively impacted the immediate and long-term outpatient utilization of dental scaling treatment in South Korea. While the policy was intended to entice patients to prevent periodontal disease, thus benefiting the insurance system, our results showed that the policy also increased treatment accessibility for potential periodontal disease patients and may improve long-term periodontal health in the South Korean population. PMID:29535886

Implementation and impact of an audit and feedback antimicrobial stewardship intervention in the orthopaedics department of a tertiary-care hospital: a controlled interrupted time series study.

Science.gov (United States)

Tavares, Margarida; Carvalho, Ana Cláudia; Almeida, José Pedro; Andrade, Paulo; São-Simão, Ricardo; Soares, Pedro; Alves, Carlos; Pinto, Rui; Fontanet, Arnaud; Watier, Laurence

2018-06-01

A prospective audit and feedback antimicrobial stewardship intervention conducted in the Orthopaedics Department of a university hospital in Portugal was evaluated by comparing an interrupted time series in the intervention group with a non-intervention (control) group. Monthly antibiotic use (except cefazolin) was measured as the World Health Organization's Anatomical Therapeutic Chemical defined daily doses (ATC-DDD) from January 2012 to September 2016, excluding the 6-month phase of intervention implementation starting on 1 January 2015. Compared with the control group, the intervention group had a monthly decrease in the use of fluoroquinolones by 2.3 DDD/1000 patient-days [95% confidence interval (CI) -3.97 to -0.63]. An increase in the use of penicillins by 103.3 DDD/1000 patient-days (95% CI 47.42 to 159.10) was associated with intervention implementation, followed by a decrease during the intervention period (slope = -5.2, 95% CI -8.56 to -1.82). In the challenging scenario of treatment of osteoarticular and prosthetic joint infections, an audit and feedback intervention reduced antibiotic exposure and spectrum. Copyright © 2018 Elsevier B.V. and International Society of Chemotherapy. All rights reserved.

Do restrictive omnibus immigration laws reduce enrollment in public health insurance by Latino citizen children? A comparative interrupted time series study.

Science.gov (United States)

Allen, Chenoa D; McNeely, Clea A

2017-10-01

In the United States, there is concern that recent state laws restricting undocumented immigrants' rights could threaten access to Medicaid and the Children's Health Insurance Program (CHIP) for citizen children of immigrant parents. Of particular concern are omnibus immigration laws, state laws that include multiple provisions increasing immigration enforcement and restricting rights for undocumented immigrants. These laws could limit Medicaid/CHIP access for citizen children in immigrant families by creating misinformation about their eligibility and fostering fear and mistrust of government among immigrant parents. This study uses nationally-representative data from the National Health Interview Survey (2005-2014; n = 70,187) and comparative interrupted time series methods to assess whether passage of state omnibus immigration laws reduced access to Medicaid/CHIP for US citizen Latino children. We found that law passage did not reduce enrollment for children with noncitizen parents and actually resulted in temporary increases in coverage among Latino children with at least one citizen parent. These findings are surprising in light of prior research. We offer potential explanations for this finding and conclude with a call for future research to be expanded in three ways: 1) examine whether policy effects vary for children of undocumented parents, compared to children whose noncitizen parents are legally present; 2) examine the joint effects of immigration-related policies at different levels, from the city or county to the state to the federal; and 3) draw on the large social movements and political mobilization literature that describes when and how Latinos and immigrants push back against restrictive immigration laws. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sequential hand hygiene promotion contributes to a reduced nosocomial bloodstream infection rate among very low-birth weight infants: An interrupted time series over a 10-year period

NARCIS (Netherlands)

Helder, O.K.; Brug, J.; van Goudoever, J.B.; Looman, C.W.N.; Reiss, I.K.M.; Kornelisse, R.F.

2014-01-01

Background Sustained high compliance with hand hygiene (HH) is needed to reduce nosocomial bloodstream infections (NBSIs). However, over time, a wash out effect often occurs. We studied the long-term effect of sequential HH-promoting interventions. Methods An observational study with an interrupted

Impact of statin related media coverage on use of statins: interrupted time series analysis with UK primary care data.

Science.gov (United States)

Matthews, Anthony; Herrett, Emily; Gasparrini, Antonio; Van Staa, Tjeerd; Goldacre, Ben; Smeeth, Liam; Bhaskaran, Krishnan

2016-06-28

 To quantify how a period of intense media coverage of controversy over the risk:benefit balance of statins affected their use.  Interrupted time series analysis of prospectively collected electronic data from primary care.  Clinical Practice Research Datalink (CPRD) in the United Kingdom.  Patients newly eligible for or currently taking statins for primary and secondary cardiovascular disease prevention in each month in January 2011-March 2015.  Adjusted odds ratios for starting/stopping taking statins after the media coverage (October 2013-March 2014).  There was no evidence that the period of high media coverage was associated with changes in statin initiation among patients with a high recorded risk score for cardiovascular disease (primary prevention) or a recent cardiovascular event (secondary prevention) (odds ratio 0.99 (95% confidence interval 0.87 to 1.13; P=0.92) and 1.04 (0.92 to 1.18; P=0.54), respectively), though there was a decrease in the overall proportion of patients with a recorded risk score. Patients already taking statins were more likely to stop taking them for both primary and secondary prevention after the high media coverage period (1.11 (1.05 to 1.18; P<0.001) and 1.12 (1.04 to 1.21; P=0.003), respectively). Stratified analyses showed that older patients and those with a longer continuous prescription were more likely to stop taking statins after the media coverage. In post hoc analyses, the increased rates of cessation were no longer observed after six months.  A period of intense public discussion over the risks:benefit balance of statins, covered widely in the media, was followed by a transient rise in the proportion of people who stopped taking statins. This research highlights the potential for widely covered health stories in the lay media to impact on healthcare related behaviour. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Adding Design Elements to Improve Time Series Designs: No Child Left behind as an Example of Causal Pattern-Matching

Science.gov (United States)

Wong, Manyee; Cook, Thomas D.; Steiner, Peter M.

2015-01-01

Some form of a short interrupted time series (ITS) is often used to evaluate state and national programs. An ITS design with a single treatment group assumes that the pretest functional form can be validly estimated and extrapolated into the postintervention period where it provides a valid counterfactual. This assumption is problematic. Ambiguous…

Effects of Interruptibility-Aware Robot Behavior

OpenAIRE

Banerjee, Siddhartha; Silva, Andrew; Feigh, Karen; Chernova, Sonia

2018-01-01

As robots become increasingly prevalent in human environments, there will inevitably be times when a robot needs to interrupt a human to initiate an interaction. Our work introduces the first interruptibility-aware mobile robot system, and evaluates the effects of interruptibility-awareness on human task performance, robot task performance, and on human interpretation of the robot's social aptitude. Our results show that our robot is effective at predicting interruptibility at high accuracy, ...

Causes of unplanned interruption of radiotherapy

International Nuclear Information System (INIS)

Diegues, Sylvia Suelotto; Ciconelli, Rozana Mesquita; Segreto, Roberto Araujo

2008-01-01

Objective: To evaluate the occurrence and causes of unplanned interruption of radiotherapy. Materials and methods: Retrospective study developed in the Division of Radiotherapy of Hospital Alemao Oswaldo Cruz in Sao Paulo, SP, Brazil, with data collected from 560 dossiers of patients submitted to radiotherapy in the period between January 1, 2005 and December 31, 2005. Chi-squared and Student t tests were utilized in the data analysis, and p < 0.05 was considered as statistically significant. Results: Interruption of treatment was identified in 350 cases, corresponding to 62.5% of the patients. The reasons for treatment interruption were the following: preventive device maintenance (55%), patient's own private reasons (13%), adverse reactions to the treatment or to combined radiotherapy/chemotherapy (6%), clinical worsening (3%), two or more combined reasons (23%). The interruption time interval ranged between 1 and 24 days (mean 1.4 day). One-day interruption was mostly due to preventive device maintenance (84.4%); two-five-day interruption was due to combined reasons (48.28%). Conclusion: The most frequent cause of interruption was preventive device maintenance, with maximum two-day time interval. (author)

Understanding Emergency Medicine Physicians Multitasking Behaviors Around Interruptions.

Science.gov (United States)

Fong, Allan; Ratwani, Raj M

2018-06-11

Interruptions can adversely impact human performance, particularly in fast-paced and high-risk environments such as the emergency department (ED). Understanding physician behaviors before, during, and after interruptions is important to the design and promotion of safe and effective workflow solutions. However, traditional human factors based interruption models do not accurately reflect the complexities of real-world environments like the ED and may not capture multiple interruptions and multitasking. We present a more comprehensive framework for understanding interruptions that is composed of three phases, each with multiple levels: Interruption Start Transition, Interruption Engagement, and Interruption End Transition. This three-phase framework is not constrained to discrete task transitions, providing a robust method to categorize multitasking behaviors around interruptions. We apply this framework in categorizing 457 interruption episodes. 457 interruption episodes were captured during 36 hours of observation. The interrupted task was immediately suspended 348 (76.1%) times. Participants engaged in new self-initiated tasks during the interrupting task 164 (35.9%) times and did not directly resume the interrupted task in 284 (62.1%) interruption episodes. Using this framework provides a more detailed description of the types of physician behaviors in complex environments. Understanding the different types of interruption and resumption patterns, which may have a different impact on performance, can support the design of interruption mitigation strategies. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Real-Time Predictions of Reservoir Size and Rebound Time during Antiretroviral Therapy Interruption Trials for HIV.

Directory of Open Access Journals (Sweden)

Alison L Hill

2016-04-01

Full Text Available Monitoring the efficacy of novel reservoir-reducing treatments for HIV is challenging. The limited ability to sample and quantify latent infection means that supervised antiretroviral therapy (ART interruption studies are generally required. Here we introduce a set of mathematical and statistical modeling tools to aid in the design and interpretation of ART-interruption trials. We show how the likely size of the remaining reservoir can be updated in real-time as patients continue off treatment, by combining the output of laboratory assays with insights from models of reservoir dynamics and rebound. We design an optimal schedule for viral load sampling during interruption, whereby the frequency of follow-up can be decreased as patients continue off ART without rebound. While this scheme can minimize costs when the chance of rebound between visits is low, we find that the reservoir will be almost completely reseeded before rebound is detected unless sampling occurs at least every two weeks and the most sensitive viral load assays are used. We use simulated data to predict the clinical trial size needed to estimate treatment effects in the face of highly variable patient outcomes and imperfect reservoir assays. Our findings suggest that large numbers of patients-between 40 and 150-will be necessary to reliably estimate the reservoir-reducing potential of a new therapy and to compare this across interventions. As an example, we apply these methods to the two "Boston patients", recipients of allogeneic hematopoietic stem cell transplants who experienced large reductions in latent infection and underwent ART-interruption. We argue that the timing of viral rebound was not particularly surprising given the information available before treatment cessation. Additionally, we show how other clinical data can be used to estimate the relative contribution that remaining HIV+ cells in the recipient versus newly infected cells from the donor made to the

The effect of health insurance and health facility-upgrades on hospital deliveries in rural Nigeria: a controlled interrupted time-series study.

Science.gov (United States)

Brals, Daniëlla; Aderibigbe, Sunday A; Wit, Ferdinand W; van Ophem, Johannes C M; van der List, Marijn; Osagbemi, Gordon K; Hendriks, Marleen E; Akande, Tanimola M; Boele van Hensbroek, Michael; Schultsz, Constance

2017-09-01

Access to quality obstetric care is considered essential to reducing maternal and new-born mortality. We evaluated the effect of the introduction of a multifaceted voluntary health insurance programme on hospital deliveries in rural Nigeria. We used an interrupted time-series design, including a control group. The intervention consisted of providing voluntary health insurance covering primary and secondary healthcare, including antenatal and obstetric care, combined with improving the quality of healthcare facilities. We compared changes in hospital deliveries from 1 May 2005 to 30 April 2013 between the programme area and control area in a difference-in-differences analysis with multiple time periods, adjusting for observed confounders. Data were collected through household surveys. Eligible households ( n = 1500) were selected from a stratified probability sample of enumeration areas. All deliveries during the 4-year baseline period ( n = 460) and 4-year follow-up period ( n = 380) were included. Insurance coverage increased from 0% before the insurance was introduced to 70.2% in April 2013 in the programme area. In the control area insurance coverage remained 0% between May 2005 and April 2013. Although hospital deliveries followed a common stable trend over the 4 pre-programme years ( P = 0.89), the increase in hospital deliveries during the 4-year follow-up period in the programme area was 29.3 percentage points (95% CI: 16.1 to 42.6; P health insurance but who could make use of the upgraded care delivered significantly more often in a hospital during the follow-up period than women living in the control area ( P = 0.04). Voluntary health insurance combined with quality healthcare services is highly effective in increasing hospital deliveries in rural Nigeria, by improving access to healthcare for insured and uninsured women in the programme area. © The Author 2017. Published by Oxford University Press in association with The London School of Hygiene and

GEKF, GUKF and GGPF based prediction of chaotic time-series with additive and multiplicative noises

International Nuclear Information System (INIS)

Wu Xuedong; Song Zhihuan

2008-01-01

On the assumption that random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables, this paper generalize the extended Kalman filtering (EKF), the unscented Kalman filtering (UKF) and the Gaussian particle filtering (GPF) to the case in which there is a positive probability that the observation in each time consists of noise alone and does not contain the chaotic signal (These generalized novel algorithms are referred to as GEKF, GUKF and GGPF correspondingly in this paper). Using weights and network output of neural networks to constitute state equation and observation equation for chaotic time-series prediction to obtain the linear system state transition equation with continuous update scheme in an online fashion, and the prediction results of chaotic time series represented by the predicted observation value, these proposed novel algorithms are applied to the prediction of Mackey–Glass time-series with additive and multiplicative noises. Simulation results prove that the GGPF provides a relatively better prediction performance in comparison with GEKF and GUKF. (general)

Analysis of Smartphone Interruptions on Academic General Internal Medicine Wards. Frequent Interruptions may cause a 'Crisis Mode' Work Climate.

Science.gov (United States)

Vaisman, Alon; Wu, Robert C

2017-01-04

Hospital-based medical services are increasingly utilizing team-based pagers and smartphones to streamline communications. However, an unintended consequence may be higher volumes of interruptions potentially leading to medical error. There is likely a level at which interruptions are excessive and cause a 'crisis mode' climate. We retrospectively collected phone, text messaging, and email interruptions directed to hospital-assigned smartphones on eight General Internal Medicine (GIM) teams at two tertiary care centres in Toronto, Ontario from April 2013 to September 2014. We also calculated the number of times these interruptions exceeded a pre-specified threshold per hour, termed 'crisis mode', defined as at least five interruptions in 30 minutes. We analyzed the correlation between interruptions and date, site, and patient volumes. A total of 187,049 interruptions were collected over an 18-month period. Daily weekday interruptions rose sharply in the morning, peaking between 11 AM to 12 PM and measuring 4.8 and 3.7 mean interruptions/hour at each site, respectively. Mean daily interruptions per team totaled 46.2 ± 3.6 at Site 1 and 39.2 ± 4.2 at Site 2. The 'crisis mode' threshold was exceeded, on average, 2.3 times/day per GIM team during weekdays. In a multivariable linear regression analysis, site (β6.43 CI95% 5.44 - 7.42, ptime.

Rotavirus vaccine impact and socioeconomic deprivation: an interrupted time-series analysis of gastrointestinal disease outcomes across primary and secondary care in the UK.

Science.gov (United States)

Hungerford, Daniel; Vivancos, Roberto; Read, Jonathan M; Iturriza-Gόmara, Miren; French, Neil; Cunliffe, Nigel A

2018-01-29

Rotavirus causes severe gastroenteritis in infants and young children worldwide. The UK introduced the monovalent rotavirus vaccine (Rotarix®) in July 2013. Vaccination is free of charge to parents, with two doses delivered at 8 and 12 weeks of age. We evaluated vaccine impact across a health system in relation to socioeconomic deprivation. We used interrupted time-series analyses to assess changes in monthly health-care attendances in Merseyside, UK, for all ages, from July 2013 to June 2016, compared to predicted counterfactual attendances without vaccination spanning 3-11 years pre-vaccine. Outcome measures included laboratory-confirmed rotavirus gastroenteritis (RVGE) hospitalisations, acute gastroenteritis (AGE) hospitalisations, emergency department (ED) attendances for gastrointestinal conditions and consultations for infectious gastroenteritis at community walk-in centres (WIC) and general practices (GP). All analyses were stratified by age. Hospitalisations were additionally stratified by vaccine uptake and small-area-level socioeconomic deprivation. The uptake of the first and second doses of rotavirus vaccine was 91.4% (29,108/31,836) and 86.7% (27,594/31,836), respectively. Among children aged impact was greatest during the rotavirus season and for vaccine-eligible age groups. In adults aged 65+ years, AGE hospitalisations fell by 25% (95% CI 19-30%; p socioeconomically deprived communities (adjusted incident rate ratio 1.57; 95% CI 1.51-1.64; p impact was greatest among the most deprived populations, despite lower vaccine uptake. Prioritising vaccine uptake in socioeconomically deprived communities should give the greatest health benefit in terms of population disease burden.

Incidence of hip and knee replacement in patients with rheumatoid arthritis following the introduction of biological DMARDs: an interrupted time-series analysis using nationwide Danish healthcare registers.

Science.gov (United States)

Cordtz, René Lindholm; Hawley, Samuel; Prieto-Alhambra, Daniel; Højgaard, Pil; Zobbe, Kristian; Overgaard, Søren; Odgaard, Anders; Kristensen, Lars Erik; Dreyer, Lene

2018-05-01

To study the impact of the introduction of biological disease-modifying anti-rheumatic drugs (bDMARDs) and associated rheumatoid arthritis (RA) management guidelines on the incidence of total hip (THR) and knee replacements (TKR) in Denmark. Nationwide register-based cohort and interrupted time-series analysis. Patients with incident RA between 1996 and 2011 were identified in the Danish National Patient Register. Patients with RA were matched on age, sex and municipality with up to 10 general population comparators (GPCs). Standardised 5-year incidence rates of THR and TKR per 1000 person-years were calculated for patients with RA and GPCs in 6-month periods. Levels and trends in the pre-bDMARD (1996-2001) were compared with the bDMARD era (2003-2016) using segmented linear regression interrupted by a 1-year lag period (2002). We identified 30 404 patients with incident RA and 297 916 GPCs. In 1996, the incidence rate of THR and TKR was 8.72 and 5.87, respectively, among patients with RA, and 2.89 and 0.42 in GPCs. From 1996 to 2016, the incidence rate of THR decreased among patients with RA, but increased among GPCs. Among patients with RA, the incidence rate of TKR increased from 1996 to 2001, but started to decrease from 2003 and throughout the bDMARD era. The incidence of TKR increased among GPCs from 1996 to 2016. We report that the incidence rate of THR and TKR was 3-fold and 14-fold higher, respectively among patients with RA compared with GPCs in 1996. In patients with RA, introduction of bDMARDs was associated with a decreasing incidence rate of TKR, whereas the incidence of THR had started to decrease before bDMARD introduction. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The effects of financial incentives for case finding for depression in patients with diabetes and coronary heart disease: interrupted time series analysis.

Science.gov (United States)

McLintock, Kate; Russell, Amy M; Alderson, Sarah L; West, Robert; House, Allan; Westerman, Karen; Foy, Robbie

2014-08-20

To evaluate the effects of Quality and Outcomes Framework (QOF) incentivised case finding for depression on diagnosis and treatment in targeted and non-targeted long-term conditions. Interrupted time series analysis. General practices in Leeds, UK. 65 (58%) of 112 general practices shared data on 37,229 patients with diabetes and coronary heart disease targeted by case finding incentives, and 101,008 patients with four other long-term conditions not targeted (hypertension, epilepsy, chronic obstructive pulmonary disease and asthma). Incentivised case finding for depression using two standard screening questions. Clinical codes indicating new depression-related diagnoses and new prescriptions of antidepressants. We extracted routinely recorded data from February 2002 through April 2012. The number of new diagnoses and prescriptions for those on registers was modelled with a binomial regression, which provided the strength of associations between time periods and their rates. New diagnoses of depression increased from 21 to 94/100,000 per month in targeted patients between the periods 2002-2004 and 2007-2011 (OR 2.09; 1.92 to 2.27). The rate increased from 27 to 77/100,000 per month in non-targeted patients (OR 1.53; 1.46 to 1.62). The slopes in prescribing for both groups flattened to zero immediately after QOF was introduced but before incentivised case finding (p<0.01 for both). Antidepressant prescribing in targeted patients returned to the pre-QOF secular upward trend (Wald test for equivalence of slope, z=0.73, p=0.47); the slope was less steep for non-targeted patients (z=-4.14, p<0.01). Incentivised case finding increased new depression-related diagnoses. The establishment of QOF disrupted rising trends in new prescriptions of antidepressants, which resumed following the introduction of incentivised case finding. Prescribing trends are of concern given that they may include people with mild-to-moderate depression unlikely to respond to such treatment

Health Facility Utilisation Changes during the Introduction of Community Case Management of Malaria in South Western Uganda: An Interrupted Time Series Approach.

Directory of Open Access Journals (Sweden)

Sham Lal

Full Text Available Malaria endemic countries have scaled-up community health worker (CHW interventions, to diagnose and treat malaria in communities with limited access to public health systems. The evaluations of these programmes have centred on CHW's compliance to guidelines, but the broader changes at public health centres including utilisation and diagnoses made, has received limited attention.This analysis was conducted during a CHW-intervention for malaria in Rukungiri District, Western Uganda. Outpatient department (OPD visit data were collected for children under-5 attending three health centres one year before the CHW-intervention started (pre-intervention period and for 20 months during the intervention (intervention-period. An interrupted time series analysis with segmented regression models was used to compare the trends in malaria, non-malaria and overall OPD visits during the pre-intervention and intervention-period.The introduction of a CHW-intervention suggested the frequency of diagnoses of diarrhoeal diseases, pneumonia and helminths increased, whilst the frequency of malaria diagnoses declined at health centres. In May 2010 when the intervention began, overall health centre utilisation decreased by 63% compared to the pre-intervention period and the health centres saw 32 fewer overall visits per month compared to the pre-intervention period (p<0.001. Malaria visits also declined shortly after the intervention began and there were 27 fewer visits per month during the intervention-period compared with the pre-intervention period (p<0.05. The declines in overall and malaria visits were sustained for the entire intervention-period. In contrast, there were no observable changes in trends of non-malarial visits between the pre-intervention and intervention-period.This analysis suggests introducing a CHW-intervention can reduce the number of child malaria visits and change the profile of cases presenting at health centres. The reduction in workload of

Current interruption by density depression

International Nuclear Information System (INIS)

Wagner, J.S.; Tajima, T.; Akasofu, S.I.

1985-04-01

Using a one-dimensional electrostatic particle code, we examine processes associated with current interruption in a collisionless plasma when a density depression is present along the current channel. Current interruption due to double layers was suggested by Alfven and Carlqvist (1967) as a cause of solar flares. At a local density depression, plasma instabilities caused by an electron current flow are accentuated, leading to current disruption. Our simulation study encompasses a wide range of the parameters in such a way that under appropriate conditions, both the Alfven and Carlqvist (1967) regime and the Smith and Priest (1972) regime take place. In the latter regime the density depression decays into a stationary structure (''ion-acoustic layer'') which spawns a series of ion-acoustic ''solitons'' and ion phase space holes travelling upstream. A large inductance of the current circuit tends to enhance the plasma instabilities

Improving Neuromuscular Monitoring and Reducing Residual Neuromuscular Blockade With E-Learning: Protocol for the Multicenter Interrupted Time Series INVERT Study.

Science.gov (United States)

Thomsen, Jakob Louis Demant; Mathiesen, Ole; Hägi-Pedersen, Daniel; Skovgaard, Lene Theil; Østergaard, Doris; Engbaek, Jens; Gätke, Mona Ring

2017-10-06

Muscle relaxants facilitate endotracheal intubation under general anesthesia and improve surgical conditions. Residual neuromuscular blockade occurs when the patient is still partially paralyzed when awakened after surgery. The condition is associated with subjective discomfort and an increased risk of respiratory complications. Use of an objective neuromuscular monitoring device may prevent residual block. Despite this, many anesthetists refrain from using the device. Efforts to increase the use of objective monitoring are time consuming and require the presence of expert personnel. A neuromuscular monitoring e-learning module might support consistent use of neuromuscular monitoring devices. The aim of the study is to assess the effect of a neuromuscular monitoring e-learning module on anesthesia staff's use of objective neuromuscular monitoring and the incidence of residual neuromuscular blockade in surgical patients at 6 Danish teaching hospitals. In this interrupted time series study, we are collecting data repeatedly, in consecutive 3-week periods, before and after the intervention, and we will analyze the effect using segmented regression analysis. Anesthesia departments in the Zealand Region of Denmark are included, and data from all patients receiving a muscle relaxant are collected from the anesthesia information management system MetaVision. We will assess the effect of the module on all levels of potential effect: staff's knowledge and skills, patient care practice, and patient outcomes. The primary outcome is use of neuromuscular monitoring in patients according to the type of muscle relaxant received. Secondary outcomes include last recorded train-of-four value, administration of reversal agents, and time to discharge from the postanesthesia care unit as well as a multiple-choice test to assess knowledge. The e-learning module was developed based on a needs assessment process, including focus group interviews, surveys, and expert opinions. The e

GPS Position Time Series @ JPL

Science.gov (United States)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Vacuum interrupters used for the interruption of high dc currents

International Nuclear Information System (INIS)

Warren, R.W.

1977-01-01

Conventional ac vacuum interrupters are being used to interrupt currents in pulsed energy storage systems. They have been tested with dc currents of up to 37 kA. The limit to the current which can be successfully interrupted has been measured as a function of various parameters. Among these are (1) the size of the interrupter, (2) the magnitude of the counterpulse current, (3) the nature and flux rating of the saturable reactor used, and (4) the kind of ''snubber'' circuit used. Fragmentary data have also been collected on electrode erosion rates and on mechanical failure of the bellows. A description is given of the circuits used in these tests and of the results found for a representative selection of the commercially available domestic interrupters. More recently efforts have been made to increase the values found for the maximum interruptible current. The techniques used have included connecting interrupters in parallel and operating them in an impressed axial magnetic field. The results of this work are discussed

Time series analysis time series analysis methods and applications

CERN Document Server

Rao, Tata Subba; Rao, C R

2012-01-01

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respect...

Highly comparative time-series analysis: the empirical structure of time series and their methods.

Science.gov (United States)

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Effect of editors' implementation of CONSORT guidelines on the reporting of abstracts in high impact medical journals: interrupted time series analysis.

Science.gov (United States)

Hopewell, Sally; Ravaud, Philippe; Baron, Gabriel; Boutron, Isabelle

2012-06-22

To investigate the effect of the CONSORT for Abstracts guidelines, and different editorial policies used by five leading general medical journals to implement the guidelines, on the reporting quality of abstracts of randomised trials. Interrupted time series analysis. We randomly selected up to 60 primary reports of randomised trials per journal per year from five high impact, general medical journals in 2006-09, if indexed in PubMed with an electronic abstract. We excluded reports that did not include an electronic abstract, and any secondary trial publications or economic analyses. We classified journals in three categories: those not mentioning the guidelines in their instructions to authors (JAMA and New England Journal of Medicine), those referring to the guidelines in their instructions to authors but with no specific policy to implement them (BMJ), and those referring to the guidelines in their instructions to authors with an active policy to implement them (Annals of Internal Medicine and Lancet). Two authors extracted data independently using the CONSORT for Abstracts checklist. Mean number of CONSORT items reported in selected abstracts, among nine items reported in fewer than 50% of the abstracts published across the five journals in 2006. We assessed 955 reports of abstracts of randomised trials. Journals with an active policy to enforce the guidelines showed an immediate increase in the level of mean number of items reported (increase of 1.50 items; P=0.0037). At 23 months after publication of the guidelines, the mean number of items reported per abstract for the primary outcome was 5.41 of nine items, a 53% increase compared with the expected level estimated on the basis of pre-intervention trends. The change in level or trend did not increase in journals with no policy to enforce the guidelines (BMJ, JAMA, and New England Journal of Medicine). Active implementation of the CONSORT for Abstracts guidelines by journals can lead to improvements in the

Introduction to Time Series Modeling

CERN Document Server

Kitagawa, Genshiro

2010-01-01

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f

Static Checking of Interrupt-driven Software

DEFF Research Database (Denmark)

Brylow, Dennis; Damgaard, Niels; Palsberg, Jens

2001-01-01

at the assembly level. In this paper we present the design and implementation of a static checker for interrupt-driven Z86-based software with hard real-time requirements. For six commercial microcontrollers, our checker has produced upper bounds on interrupt latencies and stack sizes, as well as verified...

Youth Mental Health Services Utilization Rates After a Large-Scale Social Media Campaign: Population-Based Interrupted Time-Series Analysis.

Science.gov (United States)

Booth, Richard G; Allen, Britney N; Bray Jenkyn, Krista M; Li, Lihua; Shariff, Salimah Z

2018-04-06

Despite the uptake of mass media campaigns, their overall impact remains unclear. Since 2011, a Canadian telecommunications company has operated an annual, large-scale mental health advocacy campaign (Bell Let's Talk) focused on mental health awareness and stigma reduction. In February 2012, the campaign began to explicitly leverage the social media platform Twitter and incented participation from the public by promising donations of Can $0.05 for each interaction with a campaign-specific username (@Bell_LetsTalk). The intent of the study was to examine the impact of this 2012 campaign on youth outpatient mental health services in the province of Ontario, Canada. Monthly outpatient mental health visits (primary health care and psychiatric services) were obtained for the Ontario youth aged 10 to 24 years (approximately 5.66 million visits) from January 1, 2006 to December 31, 2015. Interrupted time series, autoregressive integrated moving average modeling was implemented to evaluate the impact of the campaign on rates of monthly outpatient mental health visits. A lagged intervention date of April 1, 2012 was selected to account for the delay required for a patient to schedule and attend a mental health-related physician visit. The inclusion of Twitter into the 2012 Bell Let's Talk campaign was temporally associated with an increase in outpatient mental health utilization for both males and females. Within primary health care environments, female adolescents aged 10 to 17 years experienced a monthly increase in the mental health visit rate from 10.2/1000 in April 2006 to 14.1/1000 in April 2015 (slope change of 0.094 following campaign, Pcampaign, Pcampaign (slope change of 0.005, P=.02; slope change of 0.003, P=.005, respectively). For young adults aged 18 to 24 years, females who used primary health care experienced the most significant increases in mental health visit rates from 26.5/1000 in April 2006 to 29.2/1000 in April 2015 (slope change of 0.17 following

Associations between introduction and withdrawal of a financial incentive and timing of attendance for antenatal care and incidence of small for gestational age: natural experimental evaluation using interrupted time series methods

Science.gov (United States)

van der Waal, Zelda; Rushton, Steven; Rankin, Judith

2018-01-01

Objectives To determine whether introduction or withdrawal of a maternal financial incentive was associated with changes in timing of first attendance for antenatal care (‘booking’), or incidence of small for gestational age. Design A natural experimental evaluation using interrupted time series analysis. Setting A hospital-based maternity unit in the north of England. Participants 34 589 women (and their live-born babies) who delivered at the study hospital and completed the 25th week of pregnancy in the 75 months before (January 2003 to March 2009), 21 months during (April 2009 to December 2010) and 36 months after (January 2011 to December 2013) the incentive was available. Intervention The Health in Pregnancy Grant was a financial incentive of £190 ($235; €211) payable to pregnant women in the UK from the 25th week of pregnancy, contingent on them receiving routine antenatal care. Primary and secondary outcome measures The primary outcome was mean gestational age at booking. Secondary outcomes were proportion of women booking by 10, 18 and 25 weeks’ gestation; and proportion of babies that were small for gestational age. Results By 21 months after introduction of the grant (ie, immediately prior to withdrawal), compared with what was predicted given prior trends, there was an reduction in mean gestational age at booking of 4.8 days (95% CI 2.3 to 8.2). The comparable figure for 24 months after withdrawal was an increase of 14.0 days (95% CI 2.8 to 16.8). No changes in incidence of small for gestational age babies were seen. Conclusions The introduction of a universal financial incentive for timely attendance at antenatal care was associated with a reduction in mean gestational age at first attendance, but not the proportion of babies that were small for gestational age. Future research should explore the effects of incentives offered at different times in pregnancy and of differing values; and how stakeholders view such incentives. PMID:29391362

Associations between introduction and withdrawal of a financial incentive and timing of attendance for antenatal care and incidence of small for gestational age: natural experimental evaluation using interrupted time series methods.

Science.gov (United States)

Adams, Jean; van der Waal, Zelda; Rushton, Steven; Rankin, Judith

2018-01-31

To determine whether introduction or withdrawal of a maternal financial incentive was associated with changes in timing of first attendance for antenatal care ('booking'), or incidence of small for gestational age. A natural experimental evaluation using interrupted time series analysis. A hospital-based maternity unit in the north of England. 34 589 women (and their live-born babies) who delivered at the study hospital and completed the 25th week of pregnancy in the 75 months before (January 2003 to March 2009), 21 months during (April 2009 to December 2010) and 36 months after (January 2011 to December 2013) the incentive was available. The Health in Pregnancy Grant was a financial incentive of £190 ($235; €211) payable to pregnant women in the UK from the 25th week of pregnancy, contingent on them receiving routine antenatal care. The primary outcome was mean gestational age at booking. Secondary outcomes were proportion of women booking by 10, 18 and 25 weeks' gestation; and proportion of babies that were small for gestational age. By 21 months after introduction of the grant (ie, immediately prior to withdrawal), compared with what was predicted given prior trends, there was an reduction in mean gestational age at booking of 4.8 days (95% CI 2.3 to 8.2). The comparable figure for 24 months after withdrawal was an increase of 14.0 days (95% CI 2.8 to 16.8). No changes in incidence of small for gestational age babies were seen. The introduction of a universal financial incentive for timely attendance at antenatal care was associated with a reduction in mean gestational age at first attendance, but not the proportion of babies that were small for gestational age. Future research should explore the effects of incentives offered at different times in pregnancy and of differing values; and how stakeholders view such incentives. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No

Generalized unscented Kalman filtering based radial basis function neural network for the prediction of ground radioactivity time series with missing data

International Nuclear Information System (INIS)

Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan

2011-01-01

On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)

Reducing waiting time and raising outpatient satisfaction in a Chinese public tertiary general hospital-an interrupted time series study.

Science.gov (United States)

Sun, Jing; Lin, Qian; Zhao, Pengyu; Zhang, Qiongyao; Xu, Kai; Chen, Huiying; Hu, Cecile Jia; Stuntz, Mark; Li, Hong; Liu, Yuanli

2017-08-22

It is globally agreed that a well-designed health system deliver timely and convenient access to health services for all patients. Many interventions aiming to reduce waiting times have been implemented in Chinese public tertiary hospitals to improve patients' satisfaction. However, few were well-documented, and the effects were rarely measured with robust methods. We conducted a longitudinal study of the length of waiting times in a public tertiary hospital in Southern China which developed comprehensive data collection systems. Around an average of 60,000 outpatients and 70,000 prescribed outpatients per month were targeted for the study during Oct 2014-February 2017. We analyzed longitudinal time series data using a segmented linear regression model to assess changes in levels and trends of waiting times before and after the introduction of waiting time reduction interventions. Pearson correlation analysis was conducted to indicate the strength of association between waiting times and patient satisfactions. The statistical significance level was set at 0.05. The monthly average length of waiting time decreased 3.49 min (P = 0.003) for consultations and 8.70 min (P = 0.02) for filling prescriptions in the corresponding month when respective interventions were introduced. The trend shifted from baseline slight increasing to afterwards significant decreasing for filling prescriptions (P =0.003). There was a significant negative correlation between waiting time of filling prescriptions and outpatient satisfaction towards pharmacy services (r = -0.71, P = 0.004). The interventions aimed at reducing waiting time and raising patient satisfaction in Fujian Provincial Hospital are effective. A long-lasting reduction effect on waiting time for filling prescriptions was observed because of carefully designed continuous efforts, rather than a one-time campaign, and with appropriate incentives implemented by a taskforce authorized by the hospital managers. This

Reducing waiting time and raising outpatient satisfaction in a Chinese public tertiary general hospital-an interrupted time series study

Directory of Open Access Journals (Sweden)

Jing Sun

2017-08-01

Full Text Available Abstract Background It is globally agreed that a well-designed health system deliver timely and convenient access to health services for all patients. Many interventions aiming to reduce waiting times have been implemented in Chinese public tertiary hospitals to improve patients’ satisfaction. However, few were well-documented, and the effects were rarely measured with robust methods. Methods We conducted a longitudinal study of the length of waiting times in a public tertiary hospital in Southern China which developed comprehensive data collection systems. Around an average of 60,000 outpatients and 70,000 prescribed outpatients per month were targeted for the study during Oct 2014-February 2017. We analyzed longitudinal time series data using a segmented linear regression model to assess changes in levels and trends of waiting times before and after the introduction of waiting time reduction interventions. Pearson correlation analysis was conducted to indicate the strength of association between waiting times and patient satisfactions. The statistical significance level was set at 0.05. Results The monthly average length of waiting time decreased 3.49 min (P = 0.003 for consultations and 8.70 min (P = 0.02 for filling prescriptions in the corresponding month when respective interventions were introduced. The trend shifted from baseline slight increasing to afterwards significant decreasing for filling prescriptions (P =0.003. There was a significant negative correlation between waiting time of filling prescriptions and outpatient satisfaction towards pharmacy services (r = −0.71, P = 0.004. Conclusions The interventions aimed at reducing waiting time and raising patient satisfaction in Fujian Provincial Hospital are effective. A long-lasting reduction effect on waiting time for filling prescriptions was observed because of carefully designed continuous efforts, rather than a one-time campaign, and with appropriate incentives

Photodiode-based cutting interruption sensor for near-infrared lasers.

Science.gov (United States)

Adelmann, B; Schleier, M; Neumeier, B; Hellmann, R

2016-03-01

We report on a photodiode-based sensor system to detect cutting interruptions during laser cutting with a fiber laser. An InGaAs diode records the thermal radiation from the process zone with a ring mirror and optical filter arrangement mounted between a collimation unit and a cutting head. The photodiode current is digitalized with a sample rate of 20 kHz and filtered with a Chebyshev Type I filter. From the measured signal during the piercing, a threshold value is calculated. When the diode signal exceeds this threshold during cutting, a cutting interruption is indicated. This method is applied to sensor signals from cutting mild steel, stainless steel, and aluminum, as well as different material thicknesses and also laser flame cutting, showing the possibility to detect cutting interruptions in a broad variety of applications. In a series of 83 incomplete cuts, every cutting interruption is successfully detected (alpha error of 0%), while no cutting interruption is reported in 266 complete cuts (beta error of 0%). With this remarkable high detection rate and low error rate, the possibility to work with different materials and thicknesses in combination with the easy mounting of the sensor unit also to existing cutting machines highlight the enormous potential for this sensor system in industrial applications.

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor; Valenzuela, Olga

2017-01-01

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting.  It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of comput...

Detecting macroeconomic phases in the Dow Jones Industrial Average time series

Science.gov (United States)

Wong, Jian Cheng; Lian, Heng; Cheong, Siew Ann

2009-11-01

In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average (DJI) time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respectively. These can then be grouped into between five and seven clusters, each representing a different macroeconomic phase. The macroeconomic phases are distinguished primarily by their volatilities. We find that the US economy, as measured by the DJI, spends most of its time in a low-volatility phase and a high-volatility phase. The former can be roughly associated with economic expansion, while the latter contains the economic contraction phase in the standard economic cycle. Both phases are interrupted by a moderate-volatility market correction phase, but extremely-high-volatility market crashes are found mostly within the high-volatility phase. From the temporal distribution of various phases, we see a high-volatility phase from mid-1998 to mid-2003, and another starting mid-2007 (the current global financial crisis). Transitions from the low-volatility phase to the high-volatility phase are preceded by a series of precursor shocks, whereas the transition from the high-volatility phase to the low-volatility phase is preceded by a series of inverted shocks. The time scale for both types of transitions is about a year. We also identify the July 1997 Asian Financial Crisis to be the trigger for the mid-1998 transition, and an unnamed May 2006 market event related to corrections in the Chinese markets to be the trigger for the mid-2007 transition.

From Networks to Time Series

Science.gov (United States)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

The Effects of Interruptions on Oncologists' Patient Assessment and Medication Ordering Practices

Directory of Open Access Journals (Sweden)

Patricia L. Trbovich

2013-01-01

Full Text Available Interruptions are causal factors in medication errors. Although researchers have assessed the nature and frequency of interruptions during medication administration, there has been little focus on understanding their effects during medication ordering. The goal of this research was to examine the nature, frequency, and impact of interruptions on oncologists' ordering practices. Direct observations were conducted at a Canadian cancer treatment facility to (1 document the nature, frequency, and timing of interruptions during medication ordering, and (2 quantify the use of coping mechanisms by oncologists. On average, oncologists were interrupted 17 % of their time, and were frequently interrupted during safety-critical stages of medication ordering. When confronted with interruptions, oncologists engaged/multitasked more often than resorting to deferring/blocking. While some interruptions are necessary forms of communication, efforts must be made to reduce unnecessary interruptions during safety-critical tasks, and to develop interventions that increase oncologists' resiliency to inevitable interruptions.

Duality between Time Series and Networks

Science.gov (United States)

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Long time series

DEFF Research Database (Denmark)

Hisdal, H.; Holmqvist, E.; Hyvärinen, V.

Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the......Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the...

Immediate and sustained effects of user fee exemption on healthcare utilization among children under five in Burkina Faso: A controlled interrupted time-series analysis.

Science.gov (United States)

Zombré, David; De Allegri, Manuela; Ridde, Valéry

2017-04-01

Little is known about the long-term effects of user fee exemption policies on health care use in developing countries. We examined the association between user fee exemption and health care use among children under five in Burkina Faso. We also examined how factors related to characteristics of health facilities and their environment moderate this association. We used a multilevel controlled interrupted time-series design to examine the strength of effect and long term effects of user fee exemption policy on the rate of health service utilization in children under five between January 2004 and December 2014. The initiation of the intervention more than doubled the utilization rate with an immediate 132.596% increase in intervention facilities (IRR: 2.326; 95% CI: 1.980 to 2.672). The effect of the intervention was 32.766% higher in facilities with higher workforce density (IRR: 1.328; 95% CI (1.209-1.446)) and during the rainy season (IRR:1.2001; 95% CI: 1.0953-1.3149), but not significant in facilities with higher dispersed populations (IRR: 1.075; 95% CI: (0.942-1.207)). Although the intervention effect was substantially significant immediately following its inception, the pace of growth, while positive over a first phase, decelerated to stabilize itself three years and 7 months later before starting to decrease slowly towards the end of the study period. This study provides additional evidence to support user fee exemption policies complemented by improvements in health care quality. Future work should include an assessment of the impact of user fee exemption on infant morbidity and mortality and better discuss factors that could explain the slowdown in this upward trend of utilization rates three and a half years after the intervention onset. Copyright © 2017. Published by Elsevier Ltd.

Opioid interruptions, pain, and withdrawal symptoms in nursing home residents.

Science.gov (United States)

Redding, Sarah E; Liu, Sophia; Hung, William W; Boockvar, Kenneth S

2014-11-01

Interruptions in opioid use have the potential to cause pain relapse and withdrawal symptoms. The objectives of this study were to observe patterns of opioid interruption during acute illness in nursing home residents and examine associations between interruptions and pain and withdrawal symptoms. Patients from 3 nursing homes in a metropolitan area who were prescribed opioids were assessed for symptoms of pain and withdrawal by researchers blinded to opioid dosage received, using the Brief Pain Inventory Scale and the Clinical Opioid Withdrawal Scale, respectively, during prespecified time periods. The prespecified time periods were 2 weeks after onset of acute illness (eg, urinary tract infection), and 2 weeks after hospital admission and nursing home readmission, if they occurred. Opioid dosing was recorded and a significant interruption was defined as a complete discontinuation or a reduction in dose of >50% for ≥1 day. The covariates age, sex, race, comorbid conditions, initial opioid dose, and initial pain level were recorded. Symptoms pre- and post-opioid interruptions were compared and contrasted with those in a group without opioid interruptions. Sixty-six patients receiving opioids were followed for a mean of 10.9 months and experienced a total of 104 acute illnesses. During 64 (62%) illnesses, patients experienced any reduction in opioid dosing, with a mean (SD) dose reduction of 63.9% (29.9%). During 39 (38%) illnesses, patients experienced a significant opioid interruption. In a multivariable model, residence at 1 of the 3 nursing homes was associated with a lower risk of interruption (odds ratio = 0.073; 95% CI, 0.009 to 0.597; P pain score (difference -0.50 [2.66]; 95% CI, -3.16 to 2.16) and withdrawal score (difference -0.91 [3.12]; 95% CI, -4.03 to 2.21) after the interruption as compared with before interruption. However, when compared with patients without interruptions, patients with interruptions experienced larger increases in pain scores

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

Researches on Nutritional Behaviour in Romanian Black and White Primiparous Cows. Interruptions Number and their Duration in the Ration Consumption Time

Directory of Open Access Journals (Sweden)

Silvia Erina

2012-10-01

Full Text Available The study was carried out on 9 Romanian Black and White primiparous cows. The aim of this study was todetermine some aspect of nutritional behaviour of the cows. During the experiments, the following behaviour aspectswere determined: interruption number and their duration in the feed consumption time. Results showed that theadministration order of forages had an influence on the interruptions number, which was 0.74 less for hay in fibroussucculentorder (O1. For silage, the interruption number was 0.42 higher in fibrous-succulent order (O1. Betweenportion 1 (P1 and portion 3 (P3, the significant difference (p<0.05 was for interruptions duration, duringconsumption silage, in favour portion P1. Distinct significant differences (p<0.01 was observed for the interruptionnumber during consumption silage (0.95 sec. higher in P1 than in P3, for interruption duration (5.96 sec. higher inP1 than in P3. Between P2 and P3, significant difference (p<0.05 was observed for interruptions number duringconsumption silage and for average interruptions duration during consumption beet in favour to portion P2.Regarding the number of feedings per portion, always the differences were higher in the second feeding F1 than inthe first feeding F2.

Individual Differences in Working-Memory Capacity and Task Resumption Following Interruptions

Science.gov (United States)

Foroughi, Cyrus K.; Werner, Nicole E.; McKendrick, Ryan; Cades, David M.; Boehm-Davis, Deborah A.

2016-01-01

Previous research has shown that there is a time cost (i.e., a resumption lag) associated with resuming a task following an interruption and that the longer the duration of the interruption, the greater the time cost (i.e., resumption lag increases as interruption duration increases). The memory-for-goals model (Altmann & Trafton, 2002)…

Interrupting the Interruption: Neoliberalism and the Challenges of an Antiracist School

Science.gov (United States)

Meshulam, Assaf; Apple, Michael W.

2014-01-01

The article examines a US public elementary bilingual, multicultural school that attempts to interrupt the reproduction of existing relations of dominance and subordination across a variety of differences. The school's experiences illuminate the complex reality of schools as a site of struggle and compromise between at times contradictory…

Experiments with vacuum interrupters used for large dc-current interruption

International Nuclear Information System (INIS)

Warren, R.W.

1977-10-01

Vacuum interrupters have been tested in circuits similar to those used in theta-pinch and Tokamak fusion devices. The effects on performance of auxiliary circuit components and axial magnetic fields have been determined, and limits to lifetime caused by mechanical and electrical wear have been measured. Results show that the upper reliable limit of interruption is independent of the auxiliary components but quite dependent on interrupter size and on the axial field

Kolmogorov Space in Time Series Data

OpenAIRE

Kanjamapornkul, K.; Pinčák, R.

2016-01-01

We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for ...

On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

OpenAIRE

Trottini, Mario; Vigo, Isabel; Belda, Santiago

2015-01-01

Given a time series, running trends analysis (RTA) involves evaluating least squares trends over overlapping time windows of L consecutive time points, with overlap by all but one observation. This produces a new series called the “running trends series,” which is used as summary statistics of the original series for further analysis. In recent years, RTA has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that ...

Multiple Indicator Stationary Time Series Models.

Science.gov (United States)

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Time Series Momentum

DEFF Research Database (Denmark)

Moskowitz, Tobias J.; Ooi, Yao Hua; Heje Pedersen, Lasse

2012-01-01

We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for one to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial...... under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities...

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor

2016-01-01

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

Simulation-Based Testing of Pager Interruptions During Laparoscopic Cholecystectomy.

Science.gov (United States)

Sujka, Joseph A; Safcsak, Karen; Bhullar, Indermeet S; Havron, William S

2018-01-30

To determine if pager interruptions affect operative time, safety, or complications and management of pager issues during a simulated laparoscopic cholecystectomy. Twelve surgery resident volunteers were tested on a Simbionix Lap Mentor II simulator. Each resident performed 6 randomized simulated laparoscopic cholecystectomies; 3 with pager interruptions (INT) and 3 without pager interruptions (NO-INT). The pager interruptions were sent in the form of standardized patient vignettes and timed to distract the resident during dissection of the critical view of safety and clipping of the cystic duct. The residents were graded on a pass/fail scale for eliciting appropriate patient history and management of the pager issue. Data was extracted from the simulator for the following endpoints: operative time, safety metrics, and incidence of operative complications. The Mann-Whitney U test and contingency table analysis were used to compare the 2 groups (INT vs. NO-INT). Level I trauma center; Simulation laboratory. Twelve general surgery residents. There was no significant difference between the 2 groups in any of the operative endpoints as measured by the simulator. However, in the INT group, only 25% of the time did the surgery residents both adequately address the issue and provide effective patient management in response to the pager interruption. Pager interruptions did not affect operative time, safety, or complications during the simulated procedure. However, there were significant failures in the appropriate evaluations and management of pager issues. Consideration for diversion of patient care issues to fellow residents not operating to improve quality and safety of patient care outside the operating room requires further study. Copyright © 2018. Published by Elsevier Inc.

Time Series Analysis of Onchocerciasis Data from Mexico: A Trend towards Elimination

Science.gov (United States)

Pérez-Rodríguez, Miguel A.; Adeleke, Monsuru A.; Orozco-Algarra, María E.; Arrendondo-Jiménez, Juan I.; Guo, Xianwu

2013-01-01

Background In Latin America, there are 13 geographically isolated endemic foci distributed among Mexico, Guatemala, Colombia, Venezuela, Brazil and Ecuador. The communities of the three endemic foci found within Mexico have been receiving ivermectin treatment since 1989. In this study, we predicted the trend of occurrence of cases in Mexico by applying time series analysis to monthly onchocerciasis data reported by the Mexican Secretariat of Health between 1988 and 2011 using the software R. Results A total of 15,584 cases were reported in Mexico from 1988 to 2011. The data of onchocerciasis cases are mainly from the main endemic foci of Chiapas and Oaxaca. The last case in Oaxaca was reported in 1998, but new cases were reported in the Chiapas foci up to 2011. Time series analysis performed for the foci in Mexico showed a decreasing trend of the disease over time. The best-fitted models with the smallest Akaike Information Criterion (AIC) were Auto-Regressive Integrated Moving Average (ARIMA) models, which were used to predict the tendency of onchocerciasis cases for two years ahead. According to the ARIMA models predictions, the cases in very low number (below 1) are expected for the disease between 2012 and 2013 in Chiapas, the last endemic region in Mexico. Conclusion The endemic regions of Mexico evolved from high onchocerciasis-endemic states to the interruption of transmission due to the strategies followed by the MSH, based on treatment with ivermectin. The extremely low level of expected cases as predicted by ARIMA models for the next two years suggest that the onchocerciasis is being eliminated in Mexico. To our knowledge, it is the first study utilizing time series for predicting case dynamics of onchocerciasis, which could be used as a benchmark during monitoring and post-treatment surveillance. PMID:23459370

The Effects of Interruption Task Complexity and Interruptions on Student Multitasking

OpenAIRE

Tan, Jiun Yi

2013-01-01

Students commonly multitask while studying. The ubiquitous use of laptops and computers has facilitated this phenomenon and even changed the nature of multitasking in studying environments. Interruptions have an undeniable presence in these everyday studying environments and there are growing concerns about their potential to disrupt both performance and the learning process. Since interruptions are unavoidable, it is useful to identify the features that make some interruptions more disruptiv...

Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...

Did the Great Recession increase suicides in the USA? Evidence from an interrupted time-series analysis.

Science.gov (United States)

Harper, Sam; Bruckner, Tim A

2017-07-01

Research suggests that the Great Recession of 2007-2009 led to nearly 5000 excess suicides in the United States. However, prior work has not accounted for seasonal patterning and unique suicide trends by age and gender. We calculated monthly suicide rates from 1999 to 2013 for men and women aged 15 and above. Suicide rates before the Great Recession were used to predict the rate during and after the Great Recession. Death rates for each age-gender group were modeled using Poisson regression with robust variance, accounting for seasonal and nonlinear suicide trajectories. There were 56,658 suicide deaths during the Great Recession. Age- and gender-specific suicide trends before the recession demonstrated clear seasonal and nonlinear trajectories. Our models predicted 57,140 expected suicide deaths, leading to 482 fewer observed than expected suicides (95% confidence interval -2079, 943). We found little evidence to suggest that the Great Recession interrupted existing trajectories of suicide rates. Suicide rates were already increasing before the Great Recession for middle-aged men and women. Future studies estimating the impact of recessions on suicide should account for the diverse and unique suicide trajectories of different social groups. Copyright © 2017 Elsevier Inc. All rights reserved.

Current interruption transients calculation

CERN Document Server

Peelo, David F

2014-01-01

Provides an original, detailed and practical description of current interruption transients, origins, and the circuits involved, and how they can be calculated Current Interruption Transients Calculationis a comprehensive resource for the understanding, calculation and analysis of the transient recovery voltages (TRVs) and related re-ignition or re-striking transients associated with fault current interruption and the switching of inductive and capacitive load currents in circuits. This book provides an original, detailed and practical description of current interruption transients, origins,

Time Series with Long Memory

OpenAIRE

西埜, 晴久

2004-01-01

The paper investigates an application of long-memory processes to economic time series. We show properties of long-memory processes, which are motivated to model a long-memory phenomenon in economic time series. An FARIMA model is described as an example of long-memory model in statistical terms. The paper explains basic limit theorems and estimation methods for long-memory processes in order to apply long-memory models to economic time series.

Visibility Graph Based Time Series Analysis.

Science.gov (United States)

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Visibility Graph Based Time Series Analysis.

Directory of Open Access Journals (Sweden)

Mutua Stephen

Full Text Available Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Network structure of multivariate time series.

Science.gov (United States)

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

What marketing scholars should know about time series analysis : time series applications in marketing

NARCIS (Netherlands)

Horváth, Csilla; Kornelis, Marcel; Leeflang, Peter S.H.

2002-01-01

In this review, we give a comprehensive summary of time series techniques in marketing, and discuss a variety of time series analysis (TSA) techniques and models. We classify them in the sets (i) univariate TSA, (ii) multivariate TSA, and (iii) multiple TSA. We provide relevant marketing

Basic interrupt and command structures and applications

International Nuclear Information System (INIS)

Davies, R.C.

1974-01-01

Interrupt and command structures of a real-time system are described through specific examples. References to applications of a real-time system and programing development references are supplied. (auth)

Interruptions in emergency medicine: things are not always what they seem.

Science.gov (United States)

Walter, Scott R

2018-06-20

We have all felt the cognitive disjuncture of being interrupted during an important task. Most ED physicians will readily proffer the high frequency and/or burden of interruptions during their work, and of the many observational studies of interruptions in healthcare EDs do indeed have high interruption rates[2]. In experimental psychology, where many of these ideas originated, there is plenty of evidence that interruptions negatively affect performance. Interruptions have been associated with reduced performance on complex tasks[3,4], increased sequence errors[5], increased task completion time and augmented annoyance and anxiety[6]. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Data mining in time series databases

CERN Document Server

Kandel, Abraham; Bunke, Horst

2004-01-01

Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

Models for dependent time series

CERN Document Server

Tunnicliffe Wilson, Granville; Haywood, John

2015-01-01

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater

Muscle-invasive bladder cancer treated with external beam radiation: influence of total dose, overall treatment time, and treatment interruption on local control

International Nuclear Information System (INIS)

Moonen, L.; Voet, H. van der; Nijs, R. de; Horenblas, S.; Hart, A.A.M.; Bartelink, H.

1998-01-01

Purpose: To evaluate and eventually quantify a possible influence of tumor proliferation during the external radiation course on local control in muscle invasive bladder cancer. Methods and Materials: The influence of total dose, overall treatment time, and treatment interruption has retrospectively been analyzed in a series of 379 patients with nonmetastasized, muscle-invasive transitional cell carcinoma of the urinary bladder. All patients received external beam radiotherapy at the Netherlands Cancer Institute between 1977 and 1990. Total dose varied between 50 and 75 Gy with a mean of 60.5 Gy and a median of 60.4 Gy. Overall treatment time varied between 20 and 270 days with a mean of 49 days and a median of 41 days. Number of fractions varied between 17 and 36 with a mean of 27 and a median of 26. Two hundred and forty-four patients had a continuous radiation course, whereas 135 had an intended split course or an unintended treatment interruption. Median follow-up was 22 months for all patients and 82 months for the 30 patients still alive at last follow-up. A stepwise procedure using proportional hazard regression has been used to identify prognostic treatment factors with respect to local recurrence as sole first recurrence. Results: One hundred and thirty-six patients experienced a local recurrence and 120 of these occurred before regional or distant metastases. The actuarial local control rate was 40.3% at 5 years and 32.3% at 10 years. In a multivariate analysis total dose showed a significant association with local control (p 0.0039), however in a markedly nonlinear way. In fact only those patients treated with a dose below 57.5 Gy had a significant higher bladder relapse rate, whereas no difference in relapse rate was found among patients treated with doses above 57.5 Gy. This remained the case even after adjustment for overall treatment time and all significant tumor and patient characteristics. The Normalized Tumor Dose (NTD) (α/β = 10) and NTD (�

Visual time series analysis

DEFF Research Database (Denmark)

Fischer, Paul; Hilbert, Astrid

2012-01-01

We introduce a platform which supplies an easy-to-handle, interactive, extendable, and fast analysis tool for time series analysis. In contrast to other software suits like Maple, Matlab, or R, which use a command-line-like interface and where the user has to memorize/look-up the appropriate...... commands, our application is select-and-click-driven. It allows to derive many different sequences of deviations for a given time series and to visualize them in different ways in order to judge their expressive power and to reuse the procedure found. For many transformations or model-ts, the user may...... choose between manual and automated parameter selection. The user can dene new transformations and add them to the system. The application contains efficient implementations of advanced and recent techniques for time series analysis including techniques related to extreme value analysis and filtering...

A Review of Subsequence Time Series Clustering

Directory of Open Access Journals (Sweden)

Seyedjamal Zolhavarieh

2014-01-01

Full Text Available Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A review of subsequence time series clustering.

Science.gov (United States)

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A Review of Subsequence Time Series Clustering

Science.gov (United States)

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

Analysis of Heavy-Tailed Time Series

DEFF Research Database (Denmark)

Xie, Xiaolei

This thesis is about analysis of heavy-tailed time series. We discuss tail properties of real-world equity return series and investigate the possibility that a single tail index is shared by all return series of actively traded equities in a market. Conditions for this hypothesis to be true...... are identified. We study the eigenvalues and eigenvectors of sample covariance and sample auto-covariance matrices of multivariate heavy-tailed time series, and particularly for time series with very high dimensions. Asymptotic approximations of the eigenvalues and eigenvectors of such matrices are found...... and expressed in terms of the parameters of the dependence structure, among others. Furthermore, we study an importance sampling method for estimating rare-event probabilities of multivariate heavy-tailed time series generated by matrix recursion. We show that the proposed algorithm is efficient in the sense...

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Short-term and sustained effects of a health system strengthening intervention to improve mortality trends for paediatric severe malnutrition in rural South African hospitals: An interrupted time series design

Directory of Open Access Journals (Sweden)

M Muzigaba

2017-04-01

Full Text Available Background. Case fatality rates for childhood severe acute malnutrition (SAM remain high in some resource-limited facilities in South Africa (SA, despite the widespread availability of the World Health Organization treatment guidelines. There is a need to develop reproducible interventions that reinforce the implementation of these guidelines and assess their effect and sustainability. Objectives. To assess the short-term and sustained effects of a health system strengthening intervention on mortality attributable to SAM in two hospitals located in the Eastern Cape Province of SA. Methods. This was a theory-driven evaluation conducted in two rural hospitals in SA over a 69-month period (2009 - 2014. In both facilities, a health system strengthening intervention was implemented within the first 32 months, and thereafter discontinued. Sixty-nine monthly data series were collected on: (i monthly total SAM case fatality rate (CFR; (ii monthly SAM CFR within 24 hours of admission; and (iii monthly SAM CFR among HIV-positive cases, to determine the intervention’s effect within the first 32 months and sustainability over the remaining 37 months. The data were analysed using Linden’s method for analysing interrupted time series data. Results. The study revealed that the intervention was associated with a statistically significant decrease of up to 0.4% in monthly total SAM CFR, a non-statistically significant decrease of up to 0.09% in monthly SAM CFR within 24 hours of admission and a non-statistically significant decrease of up to 0.11% in monthly SAM CFR among HIV-positive cases. The decrease in mortality trends for both outcomes was only slightly reversed upon the discontinuation of the intervention. No autocorrelation was detected in the regression models generated during data analyses. Conclusion. The study findings suggest that although the intervention was designed to be self-sustaining, this may not have been the case. A qualitative enquiry

Impact of clinical trial findings on Bell's palsy management in general practice in the UK 2001–2012: interrupted time series regression analysis

Science.gov (United States)

Morales, Daniel R; Donnan, Peter T; Daly, Fergus; Staa, Tjeerd Van; Sullivan, Frank M

2013-01-01

Objectives To measure the incidence of Bell's palsy and determine the impact of clinical trial findings on Bell's palsy management in the UK. Design Interrupted time series regression analysis and incidence measures. Setting General practices in the UK contributing to the Clinical Practice Research Datalink (CPRD). Participants Patients ≥16 years with a diagnosis of Bell's palsy between 2001 and 2012. Interventions (1) Publication of the 2004 Cochrane reviews of clinical trials on corticosteroids and antivirals for Bell's palsy, which made no clear recommendation on their use and (2) publication of the 2007 Scottish Bell's Palsy Study (SBPS), which made a clear recommendation that treatment with prednisolone alone improves chances for complete recovery. Main outcome measures Incidence of Bell's palsy per 100 000 person-years. Changes in the management of Bell's palsy with either prednisolone therapy, antiviral therapy, combination therapy (prednisolone with antiviral therapy) or untreated cases. Results During the 12-year period, 14 460 cases of Bell's palsy were identified with an overall incidence of 37.7/100 000 person-years. The 2004 Cochrane reviews were associated with immediate falls in prednisolone therapy (−6.3% (−11.0 to −1.6)), rising trends in combination therapy (1.1% per quarter (0.5 to 1.7)) and falling trends for untreated cases (−0.8% per quarter (−1.4 to −0.3)). SBPS was associated with immediate increases in prednisolone therapy (5.1% (0.9 to 9.3)) and rising trends in prednisolone therapy (0.7% per quarter (0.4 to 1.2)); falling trends in combination therapy (−1.7% per quarter (−2.2 to −1.3)); and rising trends for untreated cases (1.2% per quarter (0.8 to 1.6)). Despite improvements, 44% still remain untreated. Conclusions SBPS was clearly associated with change in management, but a significant proportion of patients failed to receive effective treatment, which cannot be fully explained. Clarity and uncertainty in

The development, implementation and evaluation of clinical pathways for chronic obstructive pulmonary disease (COPD) in Saskatchewan: protocol for an interrupted times series evaluation.

Science.gov (United States)

Rotter, Thomas; Plishka, Christopher; Hansia, Mohammed Rashaad; Goodridge, Donna; Penz, Erika; Kinsman, Leigh; Lawal, Adegboyega; O'Quinn, Sheryl; Buchan, Nancy; Comfort, Patricia; Patel, Prakesh; Anderson, Sheila; Winkel, Tanya; Lang, Rae Lynn; Marciniuk, Darcy D

2017-11-28

Chronic obstructive pulmonary disease (COPD) has substantial economic and human costs; it is expected to be the third leading cause of death worldwide by 2030. To minimize these costs high quality guidelines have been developed. However, guidelines alone rarely result in meaningful change. One method of integrating guidelines into practice is the use of clinical pathways (CPWs). CPWs bring available evidence to a range of healthcare professionals by detailing the essential steps in care and adapting guidelines to the local context. We are working with local stakeholders to develop CPWs for COPD with the aims of improving care while reducing utilization. The CPWs will employ several steps including: standardizing diagnostic training, unifying components of chronic disease care, coordinating education and reconditioning programs, and ensuring care uses best practices. Further, we have worked to identify evidence-informed implementation strategies which will be tailored to the local context. We will conduct a three-year research project using an interrupted time series (ITS) design in the form of a multiple baseline approach with control groups. The CPW will be implemented in two health regions (experimental groups) and two health regions will act as controls (control groups). The experimental and control groups will each contain an urban and rural health region. Primary outcomes for the study will be quality of care operationalized using hospital readmission rates and emergency department (ED) presentation rates. Secondary outcomes will be healthcare utilization and guideline adherence, operationalized using hospital admission rates, hospital length of stay and general practitioner (GP) visits. Results will be analyzed using segmented regression analysis. Funding has been procured from multiple stakeholders. The project has been deemed exempt from ethics review as it is a quality improvement project. Intervention implementation is expected to begin in summer of 2017

Time Series Analysis and Forecasting by Example

CERN Document Server

Bisgaard, Soren

2011-01-01

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in

Time series with tailored nonlinearities

Science.gov (United States)

Räth, C.; Laut, I.

2015-10-01

It is demonstrated how to generate time series with tailored nonlinearities by inducing well-defined constraints on the Fourier phases. Correlations between the phase information of adjacent phases and (static and dynamic) measures of nonlinearities are established and their origin is explained. By applying a set of simple constraints on the phases of an originally linear and uncorrelated Gaussian time series, the observed scaling behavior of the intensity distribution of empirical time series can be reproduced. The power law character of the intensity distributions being typical for, e.g., turbulence and financial data can thus be explained in terms of phase correlations.

The impact of national-level interventions to improve hygiene on the incidence of irritant contact dermatitis in healthcare workers: changes in incidence from 1996 to 2012 and interrupted times series analysis.

Science.gov (United States)

Stocks, S J; McNamee, R; Turner, S; Carder, M; Agius, R M

2015-07-01

Reducing healthcare-associated infections (HCAI) has been a priority in the U.K. over recent decades and this has been reflected in interventions focusing on improving hygiene procedures. To evaluate whether these interventions coincided with an increased incidence of work-related irritant contact dermatitis (ICD) attributed to hand hygiene or/and other hygiene measures in healthcare workers (HCWs). A quasi-experimental (interrupted time series) design was used to compare trends in incidence of ICD in HCWs attributed to hygiene before and after interventions to reduce HCAI with trends in the same periods in control groups (ICD in other workers). Cases of ICD reported to a U.K. surveillance scheme from 1996 to 2012 were analysed. The time periods compared were defined objectively based on the dates of the publication of national evidence-based guidelines, the U.K. Health Act 2006 and the Cleanyourhands campaign. The reported incidence of ICD in HCWs attributed to hygiene has increased steadily from 1996 to 2012 [annual incidence rate ratio (95% confidence interval): hand hygiene only 1.10 (1.07-1.12); all hygiene 1.05 (1.03-1.07)], whereas the incidence in other workers is declining. An increase in incidence of ICD in HCWs attributed to hand hygiene was observed at the beginning of the Cleanyourhands campaign. The increasing incidence of ICD in HCWs combined with the popularity of interventions to reduce HCAI warrants increased efforts towards identifying products and implementing practices posing the least risk of ICD. © 2015 British Association of Dermatologists.

Clustering of financial time series

Science.gov (United States)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

Coping with interruptions in clinical nursing - a qualitative study

DEFF Research Database (Denmark)

Laustsen, Sussie; Brahe, Liselotte

2018-01-01

phenomenological approach. METHODS: Observations were performed combined with semi-structured qualitative interviews. RESULTS: Managing interruptions depend on level of competence, working environment, dialogue and matching of expectations, collegial roles and implicit rules. Working procedures impact on how......AIMS AND OBJECTIVES: To gain knowledge on how nurses' cope with interruptions in clinical practice. BACKGROUND: Interruptions may delay work routines and result in wasted time, disorganised planning and ineffective working procedures, affecting nurses' focus and overview in different ways. Research......: Culture work and matching of expectations are important to reflect on and discuss personal- and group behaviour caused by interruptions. We need to focus on the role of each nurse in the professional team, types of personality and unspoken rules. Professional competencies for example prioritising, keeping...

Photolytic interruptions of the bacteriorhodopsin photocycle examined by time-resolved resonance raman spectroscopy.

Science.gov (United States)

Grieger, I; Atkinson, G H

1985-09-24

An investigation of the photolytic conditions used to initiate and spectroscopically monitor the bacteriorhodopsin (BR) photocycle utilizing time-resolved resonance Raman (TR3) spectroscopy has revealed and characterized two photoinduced reactions that interrupt the thermal pathway. One reaction involves the photolytic interconversion of M-412 and M', and the other involves the direct photolytic conversion of the BR-570/K-590 photostationary mixture either to M-412 and M' or to M-like intermediates within 10 ns. The photolytic threshold conditions describing both reactions have been quantitatively measured and are discussed in terms of experimental parameters.

Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

Predicting chaotic time series

International Nuclear Information System (INIS)

Farmer, J.D.; Sidorowich, J.J.

1987-01-01

We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

Network interruptions

CERN Multimedia

2005-01-01

On Sunday 12 June 2005, a site-wide security software upgrade will be performed on all CERN network equipment. This maintenance operation will cause at least 2 short network interruptions of 2 minutes on each equipment item. There are hundreds of such items across the CERN site (Meyrin, Prévessin and all SPS and LHC pits), and it will thus take the whole day to treat them all. All network users and services will be affected. Central batch computing services will be interrupted during this period, expected to last from 8 a.m. until late evening. Job submission will still be possible but no jobs will actually be run. It is hoped to complete the computer centre upgrades in the morning so that stable access can be restored to lxplus, afs and nice services as soon as possible; this cannot be guaranteed, however. The opportunity will be used to interrupt and perform upgrades on the CERN Document Servers.

Randomized trial of time-limited interruptions of protease inhibitor-based antiretroviral therapy (ART vs. continuous therapy for HIV-1 infection.

Directory of Open Access Journals (Sweden)

Cynthia Firnhaber

Full Text Available The clinical outcomes of short interruptions of PI-based ART regimens remains undefined.A 2-arm non-inferiority trial was conducted on 53 HIV-1 infected South African participants with viral load 450 cells/µl on stavudine (or zidovudine, lamivudine and lopinavir/ritonavir. Subjects were randomized to a sequential 2, 4 and 8-week ART interruptions or b continuous ART (cART. Primary analysis was based on the proportion of CD4 count >350 cells(c/ml over 72 weeks. Adherence, HIV-1 drug resistance, and CD4 count rise over time were analyzed as secondary endpoints.The proportions of CD4 counts >350 cells/µl were 82.12% for the intermittent arm and 93.73 for the cART arm; the difference of 11.95% was above the defined 10% threshold for non-inferiority (upper limit of 97.5% CI, 24.1%; 2-sided CI: -0.16, 23.1. No clinically significant differences in opportunistic infections, adverse events, adherence or viral resistance were noted; after randomization, long-term CD4 rise was observed only in the cART arm.We are unable to conclude that short PI-based ART interruptions are non-inferior to cART in retention of immune reconstitution; however, short interruptions did not lead to a greater rate of resistance mutations or adverse events than cART suggesting that this regimen may be more forgiving than NNRTIs if interruptions in therapy occur.ClinicalTrials.gov NCT00100646.

Measuring multiscaling in financial time-series

International Nuclear Information System (INIS)

Buonocore, R.J.; Aste, T.; Di Matteo, T.

2016-01-01

We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analyzing the multi/uni-scaling behavior of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range (2, 5). We discuss the right aggregation horizon to mitigate this bias.

Time averaging, ageing and delay analysis of financial time series

Science.gov (United States)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Effect of reclassification of cannabis on hospital admissions for cannabis psychosis: a time series analysis.

Science.gov (United States)

Hamilton, Ian; Lloyd, Charlie; Hewitt, Catherine; Godfrey, Christine

2014-01-01

The UK Misuse of Drugs Act (1971) divided controlled drugs into three groups A, B and C, with descending criminal sanctions attached to each class. Cannabis was originally assigned by the Act to Group B but in 2004, it was transferred to the lowest risk group, Group C. Then in 2009, on the basis of increasing concerns about a link between high strength cannabis and schizophrenia, it was moved back to Group B. The aim of this study is to test the assumption that changes in classification lead to changes in levels of psychosis. In particular, it explores whether the two changes in 2004 and 2009 were associated with changes in the numbers of people admitted for cannabis psychosis. An interrupted time series was used to investigate the relationship between the two changes in cannabis classification and their impact on hospital admissions for cannabis psychosis. Reflecting the two policy changes, two interruptions to the time series were made. Hospital Episode Statistics admissions data was analysed covering the period 1999 through to 2010. There was a significantly increasing trend in cannabis psychosis admissions from 1999 to 2004. However, following the reclassification of cannabis from B to C in 2004, there was a significant change in the trend such that cannabis psychosis admissions declined to 2009. Following the second reclassification of cannabis back to class B in 2009, there was a significant change to increasing admissions. This study shows a statistical association between the reclassification of cannabis and hospital admissions for cannabis psychosis in the opposite direction to that predicted by the presumed relationship between the two. However, the reasons for this statistical association are unclear. It is unlikely to be due to changes in cannabis use over this period. Other possible explanations include changes in policing and systemic changes in mental health services unrelated to classification decisions. Copyright © 2013 Elsevier B.V. All rights

Applied time series analysis

CERN Document Server

Woodward, Wayne A; Elliott, Alan C

2011-01-01

""There is scarcely a standard technique that the reader will find left out … this book is highly recommended for those requiring a ready introduction to applicable methods in time series and serves as a useful resource for pedagogical purposes.""-International Statistical Review (2014), 82""Current time series theory for practice is well summarized in this book.""-Emmanuel Parzen, Texas A&M University""What an extraordinary range of topics covered, all very insightfully. I like [the authors'] innovations very much, such as the AR factor table.""-David Findley, U.S. Census Bureau (retired)""…

A series on optimizing satellite systems. I - Restoring interruptions of communications sattelite service: Logistical and cost comparisons of mature and newly operational systems

Science.gov (United States)

Snow, Marcellus S.

1989-09-01

A mathematical model is presented of costs and operational factors involved in provision for service interruptions of both a mature and typically large incumbent satellite system and of a smaller, more recently operational system. The equation expresses the required launch frequency for the new system as a function of the launch spacing of the mature system; the time disparity between the inauguration of the two systems; and the rate of capacity depreciation. In addition, a technique is presented to compare the relative extent to which the discounted costs of the new system exceed those of the mature system in furnishing the same effective capacity in orbit, and thus the same service liability, at a given point in time. It is determined that a mature incumbent communications satellite system, having more capacity in orbit, will on balance have a lower probability of service interruption than a newer, smaller system.

Deregulation of sale of over-the-counter drugs outside of pharmacies in the Republic of Korea: interrupted-time-series analysis of outpatient visits before and after the policy.

Science.gov (United States)

Chun, Sung-Youn; Park, Hye-Ki; Han, Kyu-Tae; Kim, Woorim; Lee, Hyo-Jung; Park, Eun-Cheol

2017-07-12

We evaluated the effectiveness of a policy allowing for the sale of over-the-counter drugs outside of pharmacies by examining its effect on number of monthly outpatient visits for acute upper respiratory infections, dyspepsia, and migraine. We used medical claims data extracted from the Korean National Health Insurance Cohort Database from 2009 to 2013. The Korean National Health Insurance Cohort Database comprises a nationally representative sample of claims - about 2% of the entire population - obtained from the medical record data held by the Korean National Health Insurance Corporation (which has data on the entire nation). The analysis included26,284,706 person-months of 1,042,728 individuals. An interrupted-time series analysis was performed. Outcome measures were monthly outpatient visits for acute upper respiratory infections, dyspepsia, and migraine. To investigate the effect of the policy, we compared the number of monthly visits before and after the policy's implementation in 2012. For acute upper respiratory infections, monthly outpatient visits showed a decreasing trend before the policy (ß = -0.0003);after it, a prompt change and increasing trend in monthly outpatient visits were observed, but these were non-significant. For dyspepsia, the trend was increasing before implementation (ß = -0.0101), but this reversed after implementation(ß = -0.007). For migraine, an increasing trend was observed before the policy (ß = 0.0057). After it, we observed a significant prompt change (ß = -0.0314) but no significant trend. Deregulation of selling over-the-counter medication outside of pharmacies reduced monthly outpatient visits for dyspepsia and migraine symptoms, but not acute upper respiratory infections.

Entropic Analysis of Electromyography Time Series

Science.gov (United States)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Driven to distraction: The nature and apparent purpose of interruptions in critical care and implications for HIT.

Science.gov (United States)

Mamykina, Lena; Carter, Eileen J; Sheehan, Barbara; Stanley Hum, R; Twohig, Bridget C; Kaufman, David R

2017-05-01

To examine the apparent purpose of interruptions in a Pediatric Intensive Care Unit and opportunities to reduce their burden with informatics solutions. In this prospective observational study, researchers shadowed clinicians in the unit for one hour at a time, recording all interruptions participating clinicians experienced or initiated, their starting time, duration, and a short description that could help to infer their apparent purpose. All captured interruptions were classified inductively on their source and apparent purpose and on the optimal representational media for fulfilling their apparent purpose. The researchers observed thirty-four one-hour sessions with clinicians in the unit, including 21 nurses and 13 residents and house physicians. The physicians were interrupted on average 11.9 times per hour and interrupted others 8.8 times per hour. Nurses were interrupted 8.6 times per hour and interrupted others 5.1 times per hour. The apparent purpose of interruptions included Information Seeking and Sharing (n=259, 46.3%), Directives and Requests (n=70, 12%), Shared Decision-Making (n=49, 8.8%), Direct Patient Care (n=36, 6.4%), Social (n=71, 12.7%), Device Alarms (n=28, 5%), and Non-Clinical (n=10, 1.8%); 6.6% were not classified due to insufficient description. Of all captured interruptions, 29.5% were classified as being better served with informational displays or computer-mediated communication. Deeper understanding of the purpose of interruptions in critical care can help to distinguish between interruptions that require face-to-face conversation and those that can be eliminated with informatics solutions. The proposed taxonomy of interruptions and representational analysis can be used to further advance the science of interruptions in clinical care. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantifying memory in complex physiological time-series.

Science.gov (United States)

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Impact of Frequent Interruption on Nurses' Patient-Controlled Analgesia Programming Performance.

Science.gov (United States)

Campoe, Kristi R; Giuliano, Karen K

2017-12-01

The purpose was to add to the body of knowledge regarding the impact of interruption on acute care nurses' cognitive workload, total task completion times, nurse frustration, and medication administration error while programming a patient-controlled analgesia (PCA) pump. Data support that the severity of medication administration error increases with the number of interruptions, which is especially critical during the administration of high-risk medications. Bar code technology, interruption-free zones, and medication safety vests have been shown to decrease administration-related errors. However, there are few published data regarding the impact of number of interruptions on nurses' clinical performance during PCA programming. Nine acute care nurses completed three PCA pump programming tasks in a simulation laboratory. Programming tasks were completed under three conditions where the number of interruptions varied between two, four, and six. Outcome measures included cognitive workload (six NASA Task Load Index [NASA-TLX] subscales), total task completion time (seconds), nurse frustration (NASA-TLX Subscale 6), and PCA medication administration error (incorrect final programming). Increases in the number of interruptions were associated with significant increases in total task completion time ( p = .003). We also found increases in nurses' cognitive workload, nurse frustration, and PCA pump programming errors, but these increases were not statistically significant. Complex technology use permeates the acute care nursing practice environment. These results add new knowledge on nurses' clinical performance during PCA pump programming and high-risk medication administration.

Workflow interruptions, social stressors from supervisor(s) and attention failure in surgery personnel.

Science.gov (United States)

Pereira, Diana; Müller, Patrick; Elfering, Achim

2015-01-01

Workflow interruptions and social stressors among surgery personnel may cause attention failure at work that may increase rumination about work issues during leisure time. The test of these assumptions should contribute to the understanding of exhaustion in surgery personnel and patient safety. Workflow interruptions and supervisor-related social stressors were tested to predict attention failure that predicts work-related rumination during leisure time. One hundred ninety-four theatre nurses, anaesthetists and surgeons from a Swiss University hospital participated in a cross-sectional survey. The participation rate was 58%. Structural equation modelling confirmed both indirect paths from workflow interruptions and social stressors via attention failure on rumination (both pworkflow interruptions and social stressors on rumination-could not be empirically supported. Workflow interruptions and social stressors at work are likely to trigger attention failure in surgery personnel. Work redesign and team intervention could help surgery personnel to maintain a high level of quality and patient safety and detach from work related issues to recover during leisure time.

Effective Feature Preprocessing for Time Series Forecasting

DEFF Research Database (Denmark)

Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

2006-01-01

Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

State-level gonorrhea rates and expedited partner therapy laws: insights from time series analyses.

Science.gov (United States)

Owusu-Edusei, K; Cramer, R; Chesson, H W; Gift, T L; Leichliter, J S

2017-06-01

In this study, we examined state-level monthly gonorrhea morbidity and assessed the potential impact of existing expedited partner therapy (EPT) laws in relation to the time that the laws were enacted. Longitudinal study. We obtained state-level monthly gonorrhea morbidity (number of cases/100,000 for males, females and total) from the national surveillance data. We used visual examination (of morbidity trends) and an autoregressive time series model in a panel format with intervention (interrupted time series) analysis to assess the impact of state EPT laws based on the months in which the laws were enacted. For over 84% of the states with EPT laws, the monthly morbidity trends did not show any noticeable decreases on or after the laws were enacted. Although we found statistically significant decreases in gonorrhea morbidity within four of the states with EPT laws (Alaska, Illinois, Minnesota, and Vermont), there were no significant decreases when the decreases in the four states were compared contemporaneously with the decreases in states that do not have the laws. We found no impact (decrease in gonorrhea morbidity) attributable exclusively to the EPT law(s). However, these results do not imply that the EPT laws themselves were not effective (or failed to reduce gonorrhea morbidity), because the effectiveness of the EPT law is dependent on necessary intermediate events/outcomes, including sexually transmitted infection service providers' awareness and practice, as well as acceptance by patients and their partners. Published by Elsevier Ltd.

Long term effect of reduced pack sizes of paracetamol on poisoning deaths and liver transplant activity in England and Wales: interrupted time series analyses

Science.gov (United States)

Bergen, Helen; Simkin, Sue; Dodd, Sue; Pocock, Phil; Bernal, William; Gunnell, David; Kapur, Navneet

2013-01-01

Objective To assess the long term effect of United Kingdom legislation introduced in September 1998 to restrict pack sizes of paracetamol on deaths from paracetamol poisoning and liver unit activity. Design Interrupted time series analyses to assess mean quarterly changes from October 1998 to the end of 2009 relative to projected deaths without the legislation based on pre-legislation trends. Setting Mortality (1993-2009) and liver unit activity (1995-2009) in England and Wales, using information from the Office for National Statistics and NHS Blood and Transplant, respectively. Participants Residents of England and Wales. Main outcome measures Suicide, deaths of undetermined intent, and accidental poisoning deaths involving single drug ingestion of paracetamol and paracetamol compounds in people aged 10 years and over, and liver unit registrations and transplantations for paracetamol induced hepatotoxicity. Results Compared with the pre-legislation level, following the legislation there was an estimated average reduction of 17 (95% confidence interval −25 to −9) deaths per quarter in England and Wales involving paracetamol alone (with or without alcohol) that received suicide or undetermined verdicts. This decrease represented a 43% reduction or an estimated 765 fewer deaths over the 11¼ years after the legislation. A similar effect was found when accidental poisoning deaths were included, and when a conservative method of analysis was used. This decrease was largely unaltered after controlling for a non-significant reduction in deaths involving other methods of poisoning and also suicides by all methods. There was a 61% reduction in registrations for liver transplantation for paracetamol induced hepatotoxicity (−11 (−20 to −1) registrations per quarter). But no reduction was seen in actual transplantations (−3 (−12 to 6)), nor in registrations after a conservative method of analysis was used. Conclusions UK legislation to reduce pack sizes of

A simplified prevention bundle with dual hand hygiene audit reduces early-onset ventilator-associated pneumonia in cardiovascular surgery units: An interrupted time-series analysis.

Directory of Open Access Journals (Sweden)

Kang-Cheng Su

Full Text Available To investigate the effect of a simplified prevention bundle with alcohol-based, dual hand hygiene (HH audit on the incidence of early-onset ventilation-associated pneumonia (VAP.This 3-year, quasi-experimental study with interrupted time-series analysis was conducted in two cardiovascular surgery intensive care units in a medical center. Unaware external HH audit (eHH performed by non-unit-based observers was a routine task before and after bundle implementation. Based on the realistic ICU settings, we implemented a 3-component bundle, which included: a compulsory education program, a knowing internal HH audit (iHH performed by unit-based observers, and a standardized oral care (OC protocol with 0.1% chlorhexidine gluconate. The study periods comprised 4 phases: 12-month pre-implementation phase 1 (eHH+/education-/iHH-/OC-, 3-month run-in phase 2 (eHH+/education+/iHH+/OC+, 15-month implementation phase 3 (eHH+/education+/iHH+/OC+, and 6-month post-implementation phase 4 (eHH+/education-/iHH+/OC-.A total of 2553 ventilator-days were observed. VAP incidences (events/1000 ventilator days in phase 1-4 were 39.1, 40.5, 15.9, and 20.4, respectively. VAP was significantly reduced by 59% in phase 3 (vs. phase 1, incidence rate ratio [IRR] 0.41, P = 0.002, but rebounded in phase 4. Moreover, VAP incidence was inversely correlated to compliance of OC (r2 = 0.531, P = 0.001 and eHH (r2 = 0.878, P < 0.001, but not applied for iHH, despite iHH compliance was higher than eHH compliance during phase 2 to 4. Compared to eHH, iHH provided more efficient and faster improvements for standard HH practice. The minimal compliances required for significant VAP reduction were 85% and 75% for OC and eHH (both P < 0.05, IRR 0.28 and 0.42, respectively.This simplified prevention bundle effectively reduces early-onset VAP incidence. An unaware HH compliance correlates with VAP incidence. A knowing HH audit provides better improvement in HH practice. Accordingly, we suggest

Markets and pricing for interruptible electric power

International Nuclear Information System (INIS)

Gedra, T.W.; Varaiya, P.P.

1993-01-01

The authors propose a market for interruptible, or callable, forward contracts for electric power, in which the consumer grants the power supplier the right to interrupt a given unit of load in return for a price discount. The callable forward contracts are traded continuously until the time of use. This allows recourse for those customers with uncertain demand, while risk-averse consumers can minimize their price risk by purchasing early. Callable forward contracts are simple in form, and can be directly incorporated into the utility's economic dispatch procedure

Repetitive Series Interrupter II.

Science.gov (United States)

1977-07-01

nated by other authorized documents. The citation of trade names and names of manufacturers is this report is not to be construed as official... intergrating inductor Magnet circuit load resistance Pulse-forming network load resistance Fault network load resistance Time delay between TUT fire and

Statistical criteria for characterizing irradiance time series.

Energy Technology Data Exchange (ETDEWEB)

Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

2010-10-01

We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

Homogenising time series: beliefs, dogmas and facts

Science.gov (United States)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

Science.gov (United States)

Michaels, Anthony F.; Knap, Anthony H.

1992-01-01

Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

A work observation study of nuclear medicine technologists: interruptions, resilience and implications for patient safety.

Science.gov (United States)

Larcos, George; Prgomet, Mirela; Georgiou, Andrew; Westbrook, Johanna

2017-06-01

Errors by nuclear medicine technologists during the preparation of radiopharmaceuticals or at other times can cause patient harm and may reflect the impact of interruptions, busy work environments and deficient systems or processes. We aimed to: (a) characterise the rate and nature of interruptions technologists experience and (b) identify strategies that support safety. We performed 100 hours of observation of 11 technologists at a major public hospital and measured the proportions of time spent in eight categories of work tasks, location of task, interruption rate and type and multitasking (tasks conducted in parallel). We catalogued specific safety-oriented strategies used by technologists. Technologists completed 5227 tasks and experienced 569 interruptions (mean, 4.5 times per hour; 95% CI 4.1 to 4.9). The highest interruption rate occurred when technologists were in transit between rooms (10.3 per hour (95% CI 8.3 to 12.5)). Interruptions during radiopharmaceutical preparation occurred a mean of 4.4 times per hour (95% CI 3.3 to 5.6). Most (n=426) tasks were interrupted once only and all tasks were resumed after interruption. Multitasking occurred 16.6% of the time. At least some interruptions were initiated by other technologists to convey important information and/or to render assistance. Technologists employed a variety of verbal and non-verbal strategies in all work areas (notably in the hot-lab) to minimise the impact of interruptions and optimise the safe conduct of procedures. Although most were due to individual choices, some strategies reflected overt or subliminal departmental policy. Some interruptions appear beneficial. Technologists' self-initiated strategies to support safe work practices appear to be an important element in supporting a resilient work environment in nuclear medicine. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Sound, memory and interruption

DEFF Research Database (Denmark)

Pinder, David

2016-01-01

This chapter considers how art can interrupt the times and spaces of urban development so they might be imagined, experienced and understood differently. It focuses on the construction of the M11 Link Road through north-east London during the 1990s that demolished hundreds of homes and displaced...... around a thousand people. The highway was strongly resisted and it became the site of one of the country’s longest and largest anti-road struggles. The chapter addresses specifically Graeme Miller’s sound walk LINKED (2003), which for more than a decade has been broadcasting memories and stories...... of people who were violently displaced by the road as well as those who actively sought to halt it. Attention is given to the walk’s interruption of senses of the given and inevitable in two main ways. The first is in relation to the pace of the work and its deployment of slowness and arrest in a context...

Multivariate Time Series Decomposition into Oscillation Components.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Forecasting Enrollments with Fuzzy Time Series.

Science.gov (United States)

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Forecasting Cryptocurrencies Financial Time Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely...

Forecasting Cryptocurrencies Financial Time Series

OpenAIRE

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical si...

Integrated HIV-Care Into Primary Health Care Clinics and the Influence on Diabetes and Hypertension Care: An Interrupted Time Series Analysis in Free State, South Africa Over 4 Years.

Science.gov (United States)

Rawat, Angeli; Uebel, Kerry; Moore, David; Yassi, Annalee

2018-04-15

Noncommunicable diseases (NCDs), specifically diabetes and hypertension, are rising in high HIV-burdened countries such as South Africa. How integrated HIV care into primary health care (PHC) influences NCD care is unknown. We aimed to understand whether differences existed in NCD care (pre- versus post-integration) and how changes may relate to HIV patient numbers. Public sector PHC clinics in Free State, South Africa. Using a quasiexperimental design, we analyzed monthly administrative data on 4 indicators for diabetes and hypertension (clinic and population levels) during 4 years as HIV integration was implemented in PHC. Data represented 131 PHC clinics with a catchment population of 1.5 million. We used interrupted time series analysis at ±18 and ±30 months from HIV integration in each clinic to identify changes in trends postintegration compared with those in preintegration. We used linear mixed-effect models to study relationships between HIV and NCD indicators. Patients receiving antiretroviral therapy in the 131 PHC clinics studied increased from 1614 (April 2009) to 57, 958 (April 2013). Trends in new diabetes patients on treatment remained unchanged. However, population-level new hypertensives on treatment decreased at ±30 months from integration by 6/100, 000 (SE = 3, P < 0.02) and was associated with the number of new patients with HIV on treatment at the clinics. Our findings suggest that during the implementation of integrated HIV care into PHC clinics, care for hypertensive patients could be compromised. Further research is needed to understand determinants of NCD care in South Africa and other high HIV-burdened settings to ensure patient-centered PHC.

Algorithm Design of CPCI Backboard's Interrupts Management Based on VxWorks' Multi-Tasks

Science.gov (United States)

Cheng, Jingyuan; An, Qi; Yang, Junfeng

2006-09-01

This paper begins with a brief introduction of the embedded real-time operating system VxWorks and CompactPCI standard, then gives the programming interfaces of Peripheral Controller Interface (PCI) configuring, interrupts handling and multi-tasks programming interface under VxWorks, and then emphasis is placed on the software frameworks of CPCI interrupt management based on multi-tasks. This method is sound in design and easy to adapt, ensures that all possible interrupts are handled in time, which makes it suitable for data acquisition systems with multi-channels, a high data rate, and hard real-time high energy physics.

Time series modeling, computation, and inference

CERN Document Server

Prado, Raquel

2010-01-01

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

Time Series Analysis Forecasting and Control

CERN Document Server

Box, George E P; Reinsel, Gregory C

2011-01-01

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, cl

Costationarity of Locally Stationary Time Series Using costat

OpenAIRE

Cardinali, Alessandro; Nason, Guy P.

2013-01-01

This article describes the R package costat. This package enables a user to (i) perform a test for time series stationarity; (ii) compute and plot time-localized autocovariances, and (iii) to determine and explore any costationary relationship between two locally stationary time series. Two locally stationary time series are said to be costationary if there exists two time-varying combination functions such that the linear combination of the two series with the functions produces another time...

Detecting nonlinear structure in time series

International Nuclear Information System (INIS)

Theiler, J.

1991-01-01

We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

Introduction to time series and forecasting

CERN Document Server

Brockwell, Peter J

2016-01-01

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space mod...

The mythology of anticoagulation therapy interruption for dental surgery.

Science.gov (United States)

Wahl, Michael J

2018-01-01

Continuous anticoagulation therapy is used to prevent heart attacks, strokes, and other embolic complications. When patients receiving anticoagulation therapy undergo dental surgery, a decision must be made about whether to continue anticoagulation therapy and risk bleeding complications or briefly interrupt anticoagulation therapy and increase the risk of developing embolic complications. Results from decades of studies of thousands of dental patients receiving anticoagulation therapy reveal that bleeding complications requiring more than local measures for hemostasis have been rare and never fatal. However, embolic complications (some of which were fatal and others possibly permanently debilitating) sometimes have occurred in patients whose anticoagulation therapy was interrupted for dental procedures. Although there is now virtually universal consensus among national medical and dental groups and other experts that anticoagulation therapy should not be interrupted for most dental surgery, there are still some arguments made supporting anticoagulation therapy interruption. An analysis of these arguments shows them to be based on a collection of myths and half-truths rather than on logical scientific conclusions. The time has come to stop anticoagulation therapy interruption for dental procedures. Copyright © 2018 American Dental Association. Published by Elsevier Inc. All rights reserved.

TIME SERIES ANALYSIS USING A UNIQUE MODEL OF TRANSFORMATION

Directory of Open Access Journals (Sweden)

Goran Klepac

2007-12-01

Full Text Available REFII1 model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series. An advantage of this approach of time series analysis is the linkage of different methods for time series analysis, linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. It is worth mentioning that REFII model is not a closed system, which means that we have a finite set of methods. At first, this is a model for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in a domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analysis. The advantage of REFII model is its possible application in many different areas such as finance, medicine, voice recognition, face recognition and text mining.

Stress Outcomes of Four Types of Perceived Interruptions.

Science.gov (United States)

Fletcher, Keaton A; Potter, Sean M; Telford, Britany N

2018-03-01

Objective We sought to define and measure four types of perceived interruptions and to examine their relationships with stress outcomes. Background Interruptions have been defined and measured in a variety of inconsistent ways. No study has simultaneously examined the subjective experience of all types of interruptions. Method First, we provide a synthesized definition and model of interruptions that aligns interruptions along two qualities: origin and degree of multitasking. Second, we create and validate a self-report measure of these four types of perceived interruptions within two samples (working undergraduate students and working engineers). Last, we correlate this measure with self-reported psychological and physical stress outcomes. Results Our results support the four-factor model of interruptions. Results further support the link between each of the four types of interruptions (intrusions, breaks, distractions, and a specific type of ruminations, discrepancies) and stress outcomes. Specifically, results suggest that distractions explain a unique portion of variance in stress outcomes above and beyond the shared variance explained by intrusions, breaks, and discrepancies. Conclusion The synthesized four-factor model of interruptions is an adequate representation of the overall construct of interruptions. Further, perceived interruptions can be measured and are significantly related to stress outcomes. Application Measuring interruptions by observation can be intrusive and resource intensive. Additionally, some types of interruptions may be internal and therefore unobservable. Our survey measure offers a practical alternative method for practitioners and researchers interested in the outcomes of interruptions, especially stress outcomes.

A study on DC hybrid three-phase fault current limiting interrupter for a power distribution system

International Nuclear Information System (INIS)

Shao, Hongtian; Satoh, Tomoyuki; Yamaguchi, Mitsugi; Fukui, Satoshi; Ogawa, Jun; Satoh, Takao; Ishikawa, Hiroyuki

2005-01-01

For the purpose of protecting electric power system, many researches and developments of fault current limiters are being performed. The authors studied a dc hybrid three-phase fault current limiting interrupter (FCLI) composed of a superconducting reactor and an S/N transition element, connected in series each other. The dc hybrid type fault current limiting interrupter can limit a fault current by means of the inductance of high temperature superconducting (HTS) coil together with the normal transition of HTS bulk material (HTSB). In the case of an accident, the normal transition of the bulk material can be accelerated by the magnetic field of the HTS coil. In this paper, the dc hybrid type fault current limiting interrupter for 5.5 km long 6.6 kV-600 A power distribution system is analyzed, and performances of fault current limitation and interruption are confirmed. Moreover, a reclosing operation is discussed for this power distribution system

Frontiers in Time Series and Financial Econometrics

OpenAIRE

Ling, S.; McAleer, M.J.; Tong, H.

2015-01-01

__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contrib...

Control instrumentation and data handling of heavy current inductive load interrupter

International Nuclear Information System (INIS)

Calpin, J.E.

1983-01-01

The heavy duty DC interrupter is a switching system with the ability to interrupt very high inductive currents with precise timing, work in concert with an additional number of similar systems, and withstand fast recovery voltages (30 kV) after interruption. Further, it is required to be self-protecting and the high current busses isolated to 50 kV DC and subjected to 95 kV BIL test voltages. Interruption is accomplished by the separation of vacuum interrupter contacts, which prior to counterpulse arc for milliseconds, generating horrendous noise signals of frequencies from DC to ultraviolet. Neutralization of such signals on the computer interface was effected by unique BALUN filters on 25 control and status lines. The noise abatement circuitry rationale will be discussed along with triple shielding, Hall effect current level sensing and light pipe communication between high level busses and interface HTL cards. Triggering of the isolated counterpulse circuitry will be outlined. The self-protective aspects of the system employ current sensors to reclose the interrupter if current persists for two milliseconds after counterpulse

Scale-dependent intrinsic entropies of complex time series.

Science.gov (United States)

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Elements of nonlinear time series analysis and forecasting

CERN Document Server

De Gooijer, Jan G

2017-01-01

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

An Energy-Based Similarity Measure for Time Series

Directory of Open Access Journals (Sweden)

Pierre Brunagel

2007-11-01

Full Text Available A new similarity measure, called SimilB, for time series analysis, based on the cross-ΨB-energy operator (2004, is introduced. ΨB is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED or the Pearson correlation coefficient (CC, SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of ΨB are presented. Particularly, we show that ΨB as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

Detecting chaos in irregularly sampled time series.

Science.gov (United States)

Kulp, C W

2013-09-01

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual

Executing application function calls in response to an interrupt

Science.gov (United States)

Almasi, Gheorghe; Archer, Charles J.; Giampapa, Mark E.; Gooding, Thomas M.; Heidelberger, Philip; Parker, Jeffrey J.

2010-05-11

Executing application function calls in response to an interrupt including creating a thread; receiving an interrupt having an interrupt type; determining whether a value of a semaphore represents that interrupts are disabled; if the value of the semaphore represents that interrupts are not disabled: calling, by the thread, one or more preconfigured functions in dependence upon the interrupt type of the interrupt; yielding the thread; and if the value of the semaphore represents that interrupts are disabled: setting the value of the semaphore to represent to a kernel that interrupts are hard-disabled; and hard-disabling interrupts at the kernel.

Multivariate Time Series Search

Data.gov (United States)

National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

Analysing Stable Time Series

National Research Council Canada - National Science Library

Adler, Robert

1997-01-01

We describe how to take a stable, ARMA, time series through the various stages of model identification, parameter estimation, and diagnostic checking, and accompany the discussion with a goodly number...

Neural Network Models for Time Series Forecasts

OpenAIRE

Tim Hill; Marcus O'Connor; William Remus

1996-01-01

Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

Time Series Observations in the North Indian Ocean

Digital Repository Service at National Institute of Oceanography (India)

Shenoy, D.M.; Naik, H.; Kurian, S.; Naqvi, S.W.A.; Khare, N.

Ocean and the ongoing time series study (Candolim Time Series; CaTS) off Goa. In addition, this article also focuses on the new time series initiative in the Arabian Sea and the Bay of Bengal under Sustained Indian Ocean Biogeochemistry and Ecosystem...

A Wrist-Worn Thermohaptic Device for Graceful Interruption

DEFF Research Database (Denmark)

Bolton, Frank; Jalaliniya, Shahram; Pederson, Thomas

2015-01-01

Thermal haptics is a potential system output modality for wearable devices that promises to function at the periphery of human attention. When adequately combined with existing attention-governing mechanisms of the human mind, it could be used for interrupting the human agent at a time when......-worn thermohaptic actuator for self-mitigating interruption. We then develop a prototype and perform an insightful pilot study. We frame our empirical thermohaptic experimental work in terms of Peripheral Interaction concepts and show how this new approach to Human-Computer Interaction relates to the Context...

HVDC interrupter experiments for large Magnetic Energy Transfer and Storage (METS) systems

International Nuclear Information System (INIS)

Swannack, C.E.; Haarman, R.A.; Lindsay, J.D.G.; Weldon, D.M.

1975-01-01

Proposed fusion-test reactors will require energy storage systems of hundreds of megajoules with transfer times of the order of one millisecond. The size of the energy storage submodule (and hence, the overall system cost and complexity) is directly determined by the voltage and current limits of the switch used for the energy transfer. Experiments are being conducted on high voltage dc circuit breakers as a major part of the energy storage, pulsed power program. DC circuit interruption characteristics of a commercially available ac power vacuum interrupter are discussed. Preliminary data of interruption characteristics are reported for an interrupter developed specifically to match a present METS circuit requirement

Geometric noise reduction for multivariate time series.

Science.gov (United States)

Mera, M Eugenia; Morán, Manuel

2006-03-01

We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.

BRITS: Bidirectional Recurrent Imputation for Time Series

OpenAIRE

Cao, Wei; Wang, Dong; Li, Jian; Zhou, Hao; Li, Lei; Li, Yitan

2018-01-01

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing va...

Efficient Algorithms for Segmentation of Item-Set Time Series

Science.gov (United States)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

Studies on time series applications in environmental sciences

CERN Document Server

Bărbulescu, Alina

2016-01-01

Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. .

Global Population Density Grid Time Series Estimates

Data.gov (United States)

National Aeronautics and Space Administration — Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's...

Prediction and Geometry of Chaotic Time Series

National Research Council Canada - National Science Library

Leonardi, Mary

1997-01-01

This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction...

Early counterpulse technique applied to vacuum interrupters

International Nuclear Information System (INIS)

Warren, R.W.

1979-01-01

Interruption of dc currents using counterpulse techniques is investigated with vacuum interrupters and a novel approach in which the counterpulse is applied before contact separation. Important increases have been achieved in this way in the maximum interruptible current and large reductions in contact erosion. The factors establishing these new limits are presented and ways are discussed to make further improvements to the maximum interruptible current

Sensor-Generated Time Series Events: A Definition Language

Science.gov (United States)

Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martínez, Maria Aurora; Pazos, Juan

2012-01-01

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

Correlation and multifractality in climatological time series

International Nuclear Information System (INIS)

Pedron, I T

2010-01-01

Climate can be described by statistical analysis of mean values of atmospheric variables over a period. It is possible to detect correlations in climatological time series and to classify its behavior. In this work the Hurst exponent, which can characterize correlation and persistence in time series, is obtained by using the Detrended Fluctuation Analysis (DFA) method. Data series of temperature, precipitation, humidity, solar radiation, wind speed, maximum squall, atmospheric pressure and randomic series are studied. Furthermore, the multifractality of such series is analyzed applying the Multifractal Detrended Fluctuation Analysis (MF-DFA) method. The results indicate presence of correlation (persistent character) in all climatological series and multifractality as well. A larger set of data, and longer, could provide better results indicating the universality of the exponents.

Time Series Forecasting with Missing Values

Directory of Open Access Journals (Sweden)

Shin-Fu Wu

2015-11-01

Full Text Available Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, on the other hand, may alter the original time series. In this study, we propose a novel forecasting method based on least squares support vector machine (LSSVM. We employ the input patterns with the temporal information which is defined as local time index (LTI. Time series data as well as local time indexes are fed to LSSVM for doing forecasting without imputation. We compare the forecasting performance of our method with other imputation methods. Experimental results show that the proposed method is promising and is worth further investigations.

Reconstruction of ensembles of coupled time-delay systems from time series.

Science.gov (United States)

Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P

2014-06-01

We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

The analysis of time series: an introduction

National Research Council Canada - National Science Library

Chatfield, Christopher

1989-01-01

.... A variety of practical examples are given to support the theory. The book covers a wide range of time-series topics, including probability models for time series, Box-Jenkins forecasting, spectral analysis, linear systems and system identification...

General bulk service queueing system with N-policy, multiplevacations, setup time and server breakdown without interruption

Science.gov (United States)

Sasikala, S.; Indhira, K.; Chandrasekaran, V. M.

2017-11-01

In this paper, we have considered an MX / (a,b) / 1 queueing system with server breakdown without interruption, multiple vacations, setup times and N-policy. After a batch of service, if the size of the queue is ξ (customers in the queue. After a vacation, if the server finds at least N customers waiting for service, then the server needs a setup time to start the service. After a batch of service, if the amount of waiting customers in the queue is ξ (≥ a) then the server serves a batch of min(ξ,b) customers, where b ≥ a. We derived the probability generating function of queue length at arbitrary time epoch. Further, we obtained some important performance measures.

Time series modeling in traffic safety research.

Science.gov (United States)

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

Time series prediction: statistical and neural techniques

Science.gov (United States)

Zahirniak, Daniel R.; DeSimio, Martin P.

1996-03-01

In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

Effectiveness of Multivariate Time Series Classification Using Shapelets

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2015-01-01

Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

Time-series-analysis techniques applied to nuclear-material accounting

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.; Downing, D.J.

1982-05-01

This document is designed to introduce the reader to the applications of Time Series Analysis techniques to Nuclear Material Accountability data. Time series analysis techniques are designed to extract information from a collection of random variables ordered by time by seeking to identify any trends, patterns, or other structure in the series. Since nuclear material accountability data is a time series, one can extract more information using time series analysis techniques than by using other statistical techniques. Specifically, the objective of this document is to examine the applicability of time series analysis techniques to enhance loss detection of special nuclear materials. An introductory section examines the current industry approach which utilizes inventory differences. The error structure of inventory differences is presented. Time series analysis techniques discussed include the Shewhart Control Chart, the Cumulative Summation of Inventory Differences Statistics (CUSUM) and the Kalman Filter and Linear Smoother

Clinical and epidemiological rounds. Time series

Directory of Open Access Journals (Sweden)

León-Álvarez, Alba Luz

2016-07-01

Full Text Available Analysis of time series is a technique that implicates the study of individuals or groups observed in successive moments in time. This type of analysis allows the study of potential causal relationships between different variables that change over time and relate to each other. It is the most important technique to make inferences about the future, predicting, on the basis or what has happened in the past and it is applied in different disciplines of knowledge. Here we discuss different components of time series, the analysis technique and specific examples in health research.

COMPARATIVE STUDY OF RTOS AND PRIMITIVE INTERRUPT IN EMBEDDED SYSTEM

Directory of Open Access Journals (Sweden)

Dwi Purnomo

2015-03-01

Full Text Available Multitasking is one of the most challenging issues in the automation industry which is highly depended on the embedded system. There are two methods to perform multitasking in embedded system: RTOS and primitive interrupt. The main purpose of this research is to compare the performance of R¬TOS with primitive method while concurrently undertaking multiple tasks. The system, which is able to perform various tasks, has been built to evaluate the performance of both methods. There are four tasks introduced in the system: servo task, sensor task, LED task, and LCD task. The performance of each method is indicated by the success rate of the sensor task detection. Sensor task detection will be compared with the true value which is calculated and measured manually during observation time. Observation time was varied after several iterations and the data of the iteration are recorded for both RTOS and primitive interrupt methods. The results of the conducted experiments have shown that, RTOS is more accurate than interrupt method. However, the data variance of the primitive interrupt method is narrower than RTOS. Therefore, to choose a better method, an optimization is needed to be done and each product has its own standard.

Absence or interruption of the supra-acetabular line: a subtle plain film indicator of hip pathology

International Nuclear Information System (INIS)

Major, N.M.; Helms, C.A.

1996-01-01

Objective. To show that absence or interruption of the supraacetabular line is a subtle plain film indicator of pathology in the acetabulum. Design. Nineteen hips from 17 patients with known disease processes involving the acetabulum as demonstrated by subsequent magnetic resonance imaging, bone scan or plain film follow-up were evaluated with antero-posterior (AP) plain films of the pelvis. Three additional cases were diagnosed prospectively using interruption of the supra-acetabular line as the criterion for inclusion. Fifty AP plain films of the pelvis in patients without hip pain were examined prospectively to determine normal imaging criteria. Results and conclusions. The normal supra-acetabular line measures 2-3 mm in thickness superiorly and is a thin sclerotic line in the medial aspect. In all 22 hips (with pathology) in this series, the line was interrupted or absent. Loss or interruption of the supra-acetabular line may thus be a subtle pain film indicator of a disease process involving the acetabulum. This plain film sign has not previously been reported. (orig.). With 8 figs., 1 tab

Integer-valued time series

NARCIS (Netherlands)

van den Akker, R.

2007-01-01

This thesis adresses statistical problems in econometrics. The first part contributes statistical methodology for nonnegative integer-valued time series. The second part of this thesis discusses semiparametric estimation in copula models and develops semiparametric lower bounds for a large class of

Robust Forecasting of Non-Stationary Time Series

NARCIS (Netherlands)

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable

Just-in-time preemptive single machine problem with costs of earliness/tardiness, interruption and work-in-process

Directory of Open Access Journals (Sweden)

Mohammad Kazemi

2012-04-01

Full Text Available This paper considers preemption and idle time are allowed in a single machine scheduling problem with just-in-time (JIT approach. It incorporates Earliness/Tardiness (E/T penalties, interruption penalties and holding cost of jobs which are waiting to be processed as work-in-process (WIP. Generally in non-preemptive problems, E/T penalties are a function of the completion time of the jobs. Then, we introduce a non-linear preemptive scheduling model where the earliness penalty depends on the starting time of a job. The model is liberalized by an elaborately–designed procedure to reach the optimum solution. To validate and verify the performance of proposed model, computational results are presented by solving a number of numerical examples.

Characterizing time series via complexity-entropy curves

Science.gov (United States)

Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.

2017-06-01

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

Complex network approach to fractional time series

Energy Technology Data Exchange (ETDEWEB)

Manshour, Pouya [Physics Department, Persian Gulf University, Bushehr 75169 (Iran, Islamic Republic of)

2015-10-15

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

The impact of family policy and career interruptions on women's perceptions of negative occupational consequences of full-time home care

DEFF Research Database (Denmark)

Ejrnæs, Anders

2011-01-01

for their careers. On the one hand, our findings confirm the hypothesis that long-term absence from the labour market due to full-time care has negative consequences for women's occupational careers. On the other hand, our findings show that countries with well paid leave schemes combined with access to high...... quality childcare reduce the perceived negative occupational consequences of the time spent on full-time care. This is the case independently of the duration of the career interruption due to care-giving....

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2008-01-01

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

Science.gov (United States)

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Science.gov (United States)

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

The foundations of modern time series analysis

CERN Document Server

Mills, Terence C

2011-01-01

This book develops the analysis of Time Series from its formal beginnings in the 1890s through to the publication of Box and Jenkins' watershed publication in 1970, showing how these methods laid the foundations for the modern techniques of Time Series analysis that are in use today.

Time series clustering in large data sets

Directory of Open Access Journals (Sweden)

Jiří Fejfar

2011-01-01

Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

Evaluation of the national Cleanyourhands campaign to reduce Staphylococcus aureus bacteraemia and Clostridium difficile infection in hospitals in England and Wales by improved hand hygiene: four year, prospective, ecological, interrupted time series study.

Science.gov (United States)

Stone, Sheldon Paul; Fuller, Christopher; Savage, Joan; Cookson, Barry; Hayward, Andrew; Cooper, Ben; Duckworth, Georgia; Michie, Susan; Murray, Miranda; Jeanes, Annette; Roberts, J; Teare, Louise; Charlett, Andre

2012-05-03

To evaluate the impact of the Cleanyourhands campaign on rates of hospital procurement of alcohol hand rub and soap, report trends in selected healthcare associated infections, and investigate the association between infections and procurement. Prospective, ecological, interrupted time series study from 1 July 2004 to 30 June 2008. 187 acute trusts in England and Wales. Installation of bedside alcohol hand rub, materials promoting hand hygiene and institutional engagement, regular hand hygiene audits, rolled out nationally from 1 December 2004. Quarterly (that is, every three months) rates for each trust of hospital procurement of alcohol hand rub and liquid soap; Staphylococcus aureus bacteraemia (meticillin resistant (MRSA) and meticillin sensitive (MSSA)) and Clostridium difficile infection for each trust. Associations between procurement and infection rates assessed by mixed effect Poisson regression model (which also accounted for effect of bed occupancy, hospital type, and timing of other national interventions targeting these infections). Combined procurement of soap and alcohol hand rub tripled from 21.8 to 59.8 mL per patient bed day; procurement rose in association with each phase of the campaign. Rates fell for MRSA bacteraemia (1.88 to 0.91 cases per 10,000 bed days) and C difficile infection (16.75 to 9.49 cases). MSSA bacteraemia rates did not fall. Increased procurement of soap was independently associated with reduced C difficile infection throughout the study (adjusted incidence rate ratio for 1 mL increase per patient bed day 0.993, 95% confidence interval 0.990 to 0.996; P hospital procurement of alcohol rub and soap, which the results suggest has an important role in reducing rates of some healthcare associated infections. National interventions for infection control undertaken in the context of a high profile political drive can reduce selected healthcare associated infections.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

Science.gov (United States)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Mediated interruptions of anaesthesia providers using predictions of workload from anaesthesia information management system data.

Science.gov (United States)

Epstein, R H; Dexter, F

2012-09-01

Perioperative interruptions generated electronically from anaesthesia information management systems (AIMS) can provide useful feedback, but may adversely affect task performance if distractions occur at inopportune moments. Ideally such interruptions would occur only at times when their impact would be minimal. In this study of AIMS data, we evaluated the times of comments, drugs, fluids and periodic assessments (e.g. electrocardiogram diagnosis and train-of-four) to develop recommendations for the timing of interruptions during the intraoperative period. The 39,707 cases studied were divided into intervals between: 1) enter operating room; 2) induction; 3) intubation; 4) surgical incision; and 5) end surgery. Five-minute intervals of no documentation were determined for each case. The offsets from the start of each interval when >50% of ongoing cases had completed initial documentation were calculated (MIN50). The primary endpoint for each interval was the percentage of all cases still ongoing at MIN50. Results were that the intervals from entering the operating room to induction and from induction to intubation were unsuitable for interruptions confirming prior observational studies of anaesthesia workload. At least 13 minutes after surgical incision was the most suitable time for interruptions with 92% of cases still ongoing. Timing was minimally affected by the type of anaesthesia, surgical facility, surgical service, prone positioning or scheduled case duration. The implication of our results is that for mediated interruptions, waiting at least 13 minutes after the start of surgery is appropriate. Although we used AIMS data, operating room information system data is also suitable.

Lag space estimation in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...

Time-series prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

A Time Series Forecasting Method

Directory of Open Access Journals (Sweden)

Wang Zhao-Yu

2017-01-01

Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

Stochastic nature of series of waiting times

Science.gov (United States)

Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H.; Salehi, E.; Behjat, E.; Qorbani, M.; Khazaei Nezhad, M.; Zirak, M.; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M. Reza Rahimi

2013-06-01

Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the “waiting times” series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

Deformation of contact surfaces in a vacuum interrupter after high-current interruptions

Energy Technology Data Exchange (ETDEWEB)

Wang, Haoran; Wang, Zhenxing, E-mail: zxwang@xjtu.edu.cn; Zhou, Zhipeng; Jiang, Yanjun; Wang, Jianhua; Geng, Yingsan; Liu, Zhiyuan [State Key Laboratory of Electrical Insulation and Power Equipment, Xi' an Jiaotong University, Xi' an 710049 (China)

2016-08-07

In a high-current interruption, the contact surface in a vacuum interrupter might be severely damaged by constricted vacuum arcs causing a molten area on it. As a result, a protrusion will be initiated by a transient recovery voltage after current zero, enhancing the local electric field and making breakdowns occur easier. The objective of this paper is to simulate the deformation process on the molten area under a high electric field by adopting the finite element method. A time-dependent Electrohydrodynamic model was established, and the liquid-gas interface was tracked by the level-set method. From the results, the liquid metal can be deformed to a Taylor cone if the applied electric field is above a critical value. This value is correlated to the initial geometry of the liquid metal, which increases as the size of the liquid metal decreases. Moreover, the buildup time of a Taylor cone obeys the power law t = k × E{sup −3}, where E is the initial electric field and k is a coefficient related to the material property, indicating a temporal self-similar characteristic. In addition, the influence of temperature has little impact on the deformation but has great impact on electron emission. Finally, the possible reason to initiate a delayed breakdown is associated with the deformation. The breakdown does not occur immediately when the voltage is just applied upon the gap but is postponed to several milliseconds later when the tip is formed on the liquid metal.

Efficient Approximate OLAP Querying Over Time Series

DEFF Research Database (Denmark)

Perera, Kasun Baruhupolage Don Kasun Sanjeewa; Hahmann, Martin; Lehner, Wolfgang

2016-01-01

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP...... queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume...... of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions...

A Dynamic Fuzzy Cluster Algorithm for Time Series

Directory of Open Access Journals (Sweden)

Min Ji

2013-01-01

clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

A novel weight determination method for time series data aggregation

Science.gov (United States)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

Kuznetsov, Vitaly; Mariet, Zelda

2018-01-01

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

Climate Prediction Center (CPC) Global Precipitation Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...

Climate Prediction Center (CPC) Global Temperature Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...

Recurrent Neural Network Applications for Astronomical Time Series

Science.gov (United States)

Protopapas, Pavlos

2017-06-01

The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

Early counterpulse technique applied to vacuum interrupters

International Nuclear Information System (INIS)

Warren, R.W.

1979-11-01

Interruption of dc currents using counterpulse techniques is investigated with vacuum interrupters and a novel approach in which the counterpulse is applied before contact separation. Important increases have been achieved in this way in the maximum interruptible current as well as large reductions in contact erosion. The factors establishing these new limits are presented and ways are discussed to make further improvements

Servicing a globally broadcast interrupt signal in a multi-threaded computer

Science.gov (United States)

Attinella, John E.; Davis, Kristan D.; Musselman, Roy G.; Satterfield, David L.

2015-12-29

Methods, apparatuses, and computer program products for servicing a globally broadcast interrupt signal in a multi-threaded computer comprising a plurality of processor threads. Embodiments include an interrupt controller indicating in a plurality of local interrupt status locations that a globally broadcast interrupt signal has been received by the interrupt controller. Embodiments also include a thread determining that a local interrupt status location corresponding to the thread indicates that the globally broadcast interrupt signal has been received by the interrupt controller. Embodiments also include the thread processing one or more entries in a global interrupt status bit queue based on whether global interrupt status bits associated with the globally broadcast interrupt signal are locked. Each entry in the global interrupt status bit queue corresponds to a queued global interrupt.

Contrasting Effects of Dual-task Paradigm and of Timing Interruption Paradigm in Interval Timing of the Context of Culti-modal Processing%跨通道情境下双任务范式与计时中断范式中的效应比较*

Institute of Scientific and Technical Information of China (English)

尹华站; 李丹; 袁祥勇; 黄希庭

2013-01-01

solution as it uses a blank interruption instead. The researchers consistently found a similar position and interruption effect in both paradigms (Casini & Macar, 1997; Cortin, & Remblai,, 2006; Remblai, & Cortin,, 2003). Furthermore, the results showed both the discontinuity and interference of current information processing were belong to interruption effect, but to varying extents (Cortin, & Masse, 2000; Macar, 2002). However, though the position and interruption effect were similar in the two paradigms, they have not been explored in a same stimuli series. As we know, information exchange with the outside world is not dependent on single sensory channel, but rather the interaction of cross-modal information processing. It would be valuable to explore the position and interruption effect in the context ofcross-modal processing. It would not only help to uncover the cognitive mechanism of time processing, but also have important practical values as it is more similar with daily life. Therefore, the present study was designed to investigate the position and interruption effect in the two paradigms in the cross-modal conditions. To this end, the study consisted of two experiments. In experiment 1, 2500 ms and 4500 ms were set for the target time intervals, using the same stimulus sequence (visual presentation, with aural interruption), participants were allocated to control, break and interference condition respectively. In experiment 2,the target intervals were set to 1500 ms and 2500 ms. Results of experiment 1 showed that the interruption effect is more significant in break condition regardless of target time intervals. Furthermore, under the 2500ms, position effect were found in all three conditions, whereas under the 4500ms condition, the position effect only existed in the break condition. Experiment 2 found that there was position effect consistently, regardless of the interpolation conditions or target time intervals. Besides, the interrupt effect was more

Transition Icons for Time-Series Visualization and Exploratory Analysis.

Science.gov (United States)

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

Science.gov (United States)

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Mathematical foundations of time series analysis a concise introduction

CERN Document Server

Beran, Jan

2017-01-01

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

Time series analysis in the social sciences the fundamentals

CERN Document Server

Shin, Youseop

2017-01-01

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

Data imputation analysis for Cosmic Rays time series

Science.gov (United States)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

Algorithm for Compressing Time-Series Data

Science.gov (United States)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Modeling of Volatility with Non-linear Time Series Model

OpenAIRE

Kim Song Yon; Kim Mun Chol

2013-01-01

In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

Layered Ensemble Architecture for Time Series Forecasting.

Science.gov (United States)

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Optical Cutting Interruption Sensor for Fiber Lasers

Directory of Open Access Journals (Sweden)

Benedikt Adelmann

2015-09-01

Full Text Available We report on an optical sensor system attached to a 4 kW fiber laser cutting machine to detect cutting interruptions. The sensor records the thermal radiation from the process zone with a modified ring mirror and optical filter arrangement, which is placed between the cutting head and the collimator. The process radiation is sensed by a Si and InGaAs diode combination with the detected signals being digitalized with 20 kHz. To demonstrate the function of the sensor, signals arising during fusion cutting of 1 mm stainless steel and mild steel with and without cutting interruptions are evaluated and typical signatures derived. In the recorded signals the piercing process, the laser switch on and switch off point and waiting period are clearly resolved. To identify the cutting interruption, the signals of both Si and InGaAs diodes are high pass filtered and the signal fluctuation ranges being subsequently calculated. Introducing a correction factor, we identify that only in case of a cutting interruption the fluctuation range of the Si diode exceeds the InGaAs diode. This characteristic signature was successfully used to detect 80 cutting interruptions of 83 incomplete cuts (alpha error 3.6% and system recorded no cutting interruption from 110 faultless cuts (beta error of 0. This particularly high detection rate in combination with the easy integration of the sensor, highlight its potential for cutting interruption detection in industrial applications.

Interrupting long periods of sitting: good STUFF

Directory of Open Access Journals (Sweden)

Rutten Geert M

2013-01-01

Full Text Available Abstract There is increasing evidence that sedentary behaviour is in itself a health risk, regardless of the daily amount of moderate to vigorous physical activity. Therefore, sedentary behaviour should be targeted as important health behaviour. It is known that even relatively small changes of health behaviour often require serious efforts from an individual and from people in their environment to become part of their lifestyle. Therefore, interventions to promote healthy behaviours should ideally be simple, easy to perform and easily available. Since sitting is likely to be highly habitual, confrontation with an intervention should almost automatically elicit a reaction of getting up, and thus break up and reduce sitting time. One important prerequisite for successful dissemination of such an intervention could be the use of a recognisable term relating to sedentary behaviour, which should have the characteristics of an effective brand name. To become wide spread, this term may need to meet three criteria: the “Law of the few”, the “Stickiness factor”, and the “Power of context”. For that purpose we introduce STUFF: Stand Up For Fitness. STUFF can be defined as “interrupting long sitting periods by short breaks”, for instance, interrupting sitting every 30 min by standing for at least five minutes. Even though we still need evidence to test the health-enhancing effects of interrupted sitting, we hope that the introduction of STUFF will facilitate the testing of the social, psychological and health effects of interventions to reduce sitting time.

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prewhitening of hydroclimatic time series? Implications for inferred change and variability across time scales

Science.gov (United States)

Razavi, Saman; Vogel, Richard

2018-02-01

Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.

DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

OpenAIRE

Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

2017-01-01

International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

Trend time-series modeling and forecasting with neural networks.

Science.gov (United States)

Qi, Min; Zhang, G Peter

2008-05-01

Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

Segmentation of Nonstationary Time Series with Geometric Clustering

DEFF Research Database (Denmark)

Bocharov, Alexei; Thiesson, Bo

2013-01-01

We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

Non-parametric characterization of long-term rainfall time series

Science.gov (United States)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

Time Series Decomposition into Oscillation Components and Phase Estimation.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2015-01-01

Praise for the First Edition ""…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics."" -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.    Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both

Multi-Scale Dissemination of Time Series Data

DEFF Research Database (Denmark)

Guo, Qingsong; Zhou, Yongluan; Su, Li

2013-01-01

In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber......, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time...

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

Science.gov (United States)

Stránský, V; Thinová, L

2017-11-01

In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Similarity estimators for irregular and age uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2013-09-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Similarity estimators for irregular and age-uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2014-01-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Robust Forecasting of Non-Stationary Time Series

OpenAIRE

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estima...

Time Series Econometrics for the 21st Century

Science.gov (United States)

Hansen, Bruce E.

2017-01-01

The field of econometrics largely started with time series analysis because many early datasets were time-series macroeconomic data. As the field developed, more cross-sectional and longitudinal datasets were collected, which today dominate the majority of academic empirical research. In nonacademic (private sector, central bank, and governmental)…

Effectiveness of firefly algorithm based neural network in time series ...

African Journals Online (AJOL)

Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

Time Series Analysis of Insar Data: Methods and Trends

Science.gov (United States)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Energy expenditure of interruptions to sedentary behavior

Directory of Open Access Journals (Sweden)

Strath Scott J

2011-06-01

Full Text Available Abstract Background Advances in technology, social influences and environmental attributes have resulted in substan-tial portions of the day spent in sedentary pursuits. Sedentary behavior may be a cause of many chronic diseases including obesity, insulin resistance, type 2 diabetes and the metabolic syndrome. Research demonstrated that breaking up sedentary time was beneficially associated with markers of body composition, cardiovascular health and type 2 diabetes. Therefore, the purpose of this study was to quantify the total energy expenditure of three different durations of physical activity within a 30-minute sedentary period and to examine the potential benefits of interrupting sedentary behavior with physical activity for weight control. Methods Participants completed four consecutive 30-minute bouts of sedentary behavior (reading, working on the computer, or doing other desk activities with and without interruptions of walking at a self-selected pace. Bout one contained no walking interruptions. Bout two contained a 1-minute walking period. Bout three contained a 2-minute walking period. Bout four contained a 5-minute walking period. Body composition and resting metabolic rate were assessed. Result Twenty males and females (18-39 years completed this study. Results of the repeated measures analysis of variance with post-hoc testing showed that significantly more energy was expended during each 30 minute sedentary bout with a walking break than in the 30 minute sedentary bout (p Conclusions This study demonstrated that making small changes, such as taking a five minute walking break every hour could yield beneficial weight control or weight loss results. Therefore, taking breaks from sedentary time is a potential outlet to prevent obesity and the rise of obesity in developed countries.

Interpretation of a compositional time series

Science.gov (United States)

Tolosana-Delgado, R.; van den Boogaart, K. G.

2012-04-01

Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA

Capturing Structure Implicitly from Time-Series having Limited Data

OpenAIRE

Emaasit, Daniel; Johnson, Matthew

2018-01-01

Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data are quite noisy. This presents a major challenge when estimating time-series models that are robust to overfitting and have well-calibrated uncertainty estimates. Most of the current literature in these fields involve visualizing the time-series for noticeabl...

Self-affinity in the dengue fever time series

Science.gov (United States)

Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.

2016-06-01

Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.

Interrupter and hybrid-switch testing for fusion devices

International Nuclear Information System (INIS)

Parsons, W.M.; Warren, R.W.; Honig, E.M.; Lindsay, J.D.G.; Bellamo, P.; Cassel, R.L.

1979-01-01

This paper discusses recent and ongoing switch testing for fusion devices. The first part describes testing for the TFTR ohmic-heating circuit. In this set of tests, which simulated the stresses produced during a plasma initiation pulse, circuit breakers were required to interrupt a current of 24 kA with an associated recovery voltage of 25 kV. Two interrupter systems were tested for over 1000 operations each, and both appear to satisfy TFTR requirements. The second part discusses hybrid-switch development for superconducting coil protection. These switching systems must be capable of carrying large currents on a continuous basis as well as performing interruption duties. The third part presents preliminary results on an early-counterpulse technique applied to vacuum interrupters. Implementation of this technique has resulted in large increases in interruptible current as well as a marked reduction in contact erosion

Effect of growth interruption on the crystalline quality and electrical properties of Ga-doped ZnO thin film deposited on quartz substrate by magnetron sputtering

International Nuclear Information System (INIS)

Lee, Geun-Hyoung

2013-01-01

Ga-doped ZnO(GZO) thin films were deposited on the quartz substrate by magnetron sputtering system with growth interruption technique. As the number of interruptions and interruption time increased, the carrier concentration and Hall mobility in GZO films significantly increased. As a result, the resistivity of GZO films decreased. The optical transmittance of GZO films also increased with the number of interruption and interruption time. The transmittance showed over 90% in visual region. Atomic force microscopy measurement showed that the film surface became smoother with an increase of the number of interruption. In addition, the crystalline quality and electrical properties of GZO films were more improved when the growth interruption was employed with a temperature gradient. - Highlights: • Ga-doped ZnO thin films were deposited with growth interruption technique. • The crystallinity of the films was improved with the number of interruptions. • The crystallinity of the films was improved as the interruption time increased. • The growth interruption with a temperature gradient more improved the film quality

On the plurality of times: disunified time and the A-series | Nefdt ...

African Journals Online (AJOL)

Then, I attempt to show that disunified time is a problem for a semantics based on the A-series since A-truthmakers are hard to come by in a universe of temporally disconnected time-series. Finally, I provide a novel argument showing that presentists should be particularly fearful of such a universe. South African Journal of ...

Time-series modeling of long-term weight self-monitoring data.

Science.gov (United States)

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

Time series prediction of apple scab using meteorological ...

African Journals Online (AJOL)

A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different ...

Characterization of time series via Rényi complexity-entropy curves

Science.gov (United States)

Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2018-05-01

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

Quantifying Selection with Pool-Seq Time Series Data.

Science.gov (United States)

Taus, Thomas; Futschik, Andreas; Schlötterer, Christian

2017-11-01

Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Transformation-cost time-series method for analyzing irregularly sampled data.

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Transformation-cost time-series method for analyzing irregularly sampled data

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

A multidisciplinary database for geophysical time series management

Science.gov (United States)

Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

2013-12-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

Effect of Radiotherapy Interruptions on Survival in Medicare Enrollees With Local and Regional Head-and-Neck Cancer

International Nuclear Information System (INIS)

Fesinmeyer, Megan Dann; Mehta, Vivek; Blough, David; Tock, Lauri; Ramsey, Scott D.

2010-01-01

Purpose: To investigate whether interruptions in radiotherapy are associated with decreased survival in a population-based sample of head-and-neck cancer patients. Methods and Materials: Using the Surveillance, Epidemiology, and End Results-Medicare linked database we identified Medicare beneficiaries aged 66 years and older diagnosed with local-regional head-and-neck cancer during the period 1997-2003. We examined claims records of 3864 patients completing radiotherapy for the presence of one or more 5-30-day interruption(s) in therapy. We then performed Cox regression analyses to estimate the association between therapy interruptions and survival. Results: Patients with laryngeal tumors who experienced an interruption in radiotherapy had a 68% (95% confidence interval, 41-200%) increased risk of death, compared with patients with no interruptions. Patients with nasal cavity, nasopharynx, oral, salivary gland, and sinus tumors had similar associations between interruptions and increased risk of death, but these did not reach statistical significance because of small sample sizes. Conclusions: Treatment interruptions seem to influence survival time among patients with laryngeal tumors completing a full course of radiotherapy. At all head-and-neck sites, the association between interruptions and survival is sensitive to confounding by stage and other treatments. Further research is needed to develop methods to identify patients most susceptible to interruption-induced mortality.

Modeling financial time series with S-plus

CERN Document Server

Zivot, Eric

2003-01-01

The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...

Design and test of a 40-kV, 80-A, 10-msec, neutral-beam power supply series

International Nuclear Information System (INIS)

North, G.G.

1977-01-01

To meet neutral-beam source requirements, a combination series switch/regulator system has been developed that can provide up to 40-kV at 80A output for 10-ms from the continuously decaying voltage of a charged capacitor bank. The system uses 100% feedback control of a series hard tube regulator. This feedback regulator is able to maintain a 40-kV output level for 100% load variations while the source voltage for the capacitor bank is drained from an initial 55-kV down to as low as 43-kV during a 10-ms pulse. In addition to controlling the output voltage, the series regulator tube also serves the dual role of a disconnect or interrupt switch at the end of each pulse and during the frequent occurrence of a neutral-beam source fault. In the interrupt mode, complete disconnect is achieved in less than 2-μs after first observance of a fault condition; recovery times to normal operation of less than 10-μs after fault clearance can be attained if desired

Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin

Directory of Open Access Journals (Sweden)

Seied Yahya Mirzaee

2005-11-01

    One of the important issues that have significant role in study of hydrology of basin is determination of lag time. Lag time has significant role in hydrological studies. Quantity of rainfall related lag time depends on several factors, such as permeability, vegetation cover, catchments slope, rainfall intensity, storm duration and type of rain. Determination of lag time is important parameter in many projects such as dam design and also water resource studies. Lag time of basin could be calculated using various methods. One of these methods is time series analysis of spectral density. The analysis is based on fouries series. The time series is approximated with Sinuous and Cosines functions. In this method harmonically significant quantities with individual frequencies are presented. Spectral density under multiple time series could be used to obtain basin lag time for annual runoff and short-term rainfall fluctuation. A long lag time could be due to snowmelt as well as melting ice due to rainfalls in freezing days. In this research the lag time of Jahanbin basin has been determined using spectral density method. The catchments is subjected to both rainfall and snowfall. For short term rainfall fluctuation with a return period  2, 3, 4 months, the lag times were found 0.18, 0.5 and 0.083 month, respectively.

Modeling Time Series Data for Supervised Learning

Science.gov (United States)

Baydogan, Mustafa Gokce

2012-01-01

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

Short and long term variability of the interrupter technique under field and standardised conditions in 3-6 year old children

NARCIS (Netherlands)

Beelen, RMJ; Smit, HA; van Strien, RT; Koopman, LP; Brussee, JE; Brunekreef, B; Gerritsen, J; Merkus, PJFM

2003-01-01

Background: The short and long term variability of the interrupter technique was assessed to determine whether interrupter resistance is a stable individual characteristic over time. The effect of field and standardised measurement conditions on the within-subject variability of the interrupter

Empirical method to measure stochasticity and multifractality in nonlinear time series

Science.gov (United States)

Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

2013-12-01

An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

The Effect of Integration of Self-Management Web Platforms on Health Status in Chronic Obstructive Pulmonary Disease Management in Primary Care (e-Vita Study): Interrupted Time Series Design.

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Talboom-Kamp, Esther Pwa; Verdijk, Noortje A; Kasteleyn, Marise J; Harmans, Lara M; Talboom, Irvin Jsh; Looijmans-van den Akker, Ingrid; van Geloven, Nan; Numans, Mattijs E; Chavannes, Niels H

2017-08-16

Worldwide nearly 3 million people die from chronic obstructive pulmonary disease (COPD) every year. Integrated disease management (IDM) improves quality of life for COPD patients and can reduce hospitalization. Self-management of COPD through eHealth is an effective method to improve IDM and clinical outcomes. The objective of this implementation study was to investigate the effect of 3 chronic obstructive pulmonary disease eHealth programs applied in primary care on health status. The e-Vita COPD study compares different levels of integration of Web-based self-management platforms in IDM in 3 primary care settings. Patient health status is examined using the Clinical COPD Questionnaire (CCQ). The parallel cohort design includes 3 levels of integration in IDM (groups 1, 2, 3) and randomization of 2 levels of personal assistance for patients (group A, high assistance, group B, low assistance). Interrupted time series (ITS) design was used to collect CCQ data at multiple time points before and after intervention, and multilevel linear regression modeling was used to analyze CCQ data. Of the 702 invited patients, 215 (30.6%) registered to a platform. Of these, 82 participated in group 1 (high integration IDM), 36 in group 1A (high assistance), and 46 in group 1B (low assistance); 96 participated in group 2 (medium integration IDM), 44 in group 2A (high assistance) and 52 in group 2B (low assistance); also, 37 participated in group 3 (no integration IDM). In the total group, no significant difference was found in change in CCQ trend (P=.334) before (-0.47% per month) and after the intervention (-0.084% per month). Also, no significant difference was found in CCQ changes before versus after the intervention between the groups with high versus low personal assistance. In all subgroups, there was no significant change in the CCQ trend before and after the intervention (group 1A, P=.237; 1B, P=.991; 2A, P=.120; 2B, P=.166; 3, P=.945). The e-Vita eHealth-supported COPD

Clinical time series prediction: Toward a hierarchical dynamical system framework.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Clinical time series prediction: towards a hierarchical dynamical system framework

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

Turbulencelike Behavior of Seismic Time Series

International Nuclear Information System (INIS)

Manshour, P.; Saberi, S.; Sahimi, Muhammad; Peinke, J.; Pacheco, Amalio F.; Rahimi Tabar, M. Reza

2009-01-01

We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes

Characterizing time series: when Granger causality triggers complex networks

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Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Characterizing time series: when Granger causality triggers complex networks

International Nuclear Information System (INIS)

Ge Tian; Cui Yindong; Lin Wei; Liu Chong; Kurths, Jürgen

2012-01-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length. (paper)

Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

Interruptions and multitasking in surgery: a multicentre observational study of the daily work patterns of doctors and nurses.

Science.gov (United States)

Bellandi, Tommaso; Cerri, Alessandro; Carreras, Giulia; Walter, Scott; Mengozzi, Cipriana; Albolino, Sara; Mastrominico, Eleonora; Renzetti, Fernando; Tartaglia, Riccardo; Westbrook, Johanna

2018-01-01

The aim of this study was to obtain baseline data on doctors' and nurses' work activities and rates of interruptions and multitasking to improve work organisation and processes. Data were collected in six surgical units with the WOMBAT (Work Observation Method by Activity Timing) tool. Results show that doctors and nurses received approximately 13 interruptions per hour, or one interruption every 4.5 min. Compared to doctors, nurses were more prone to interruptions in most activities, while doctors performed multitasking (33.47% of their time, 95% CI 31.84-35.17%) more than nurses (15.23%, 95% CI 14.24-16.25%). Overall, the time dedicated to patient care is relatively limited for both professions (37.21%, 95% CI 34.95-39.60% for doctors, 27.22%, 95% CI 25.18-29.60% for nurses) compared to the time spent for registration of data and professional communication, that accounts for two-thirds of doctors' time and nearly half of nurses' time. Further investigation is needed on strategies to manage job demands and professional communications. Practitioner Summary: This study offers further findings on the characteristics and frequency of multitasking and interruptions in surgery, with a comparison of how they affect doctors and nurses. Further investigation is needed to improve the management of job demands and communications according to the results.

Measurements of spatial population synchrony: influence of time series transformations.

Science.gov (United States)

Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

2015-09-01

Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

The Effects of Career Interruptions on Young Men and Women.

Science.gov (United States)

Shorten, Brett; Lewis, Donald E.

1991-01-01

Data from a sample of 5,837 Australians showed that (1) women had longer career interruptions; (2) regardless of number of interruptions, men had higher wages; (3) longer interruptions had a negative effect on reentry wages; and (4) 1985-88 growth in wages for males was enhanced by increased numbers and length of interruptions, with the opposite…

Stochastic time series analysis of hydrology data for water resources

Science.gov (United States)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

Neural network versus classical time series forecasting models

Science.gov (United States)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Nonlinear time series analysis of the human electrocardiogram

International Nuclear Information System (INIS)

Perc, Matjaz

2005-01-01

We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

Science.gov (United States)

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

Zucchini, Walter

2009-01-01

Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.

Constructing ordinal partition transition networks from multivariate time series.

Science.gov (United States)

Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

2017-08-10

A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

Permutation entropy of finite-length white-noise time series.

Science.gov (United States)

Little, Douglas J; Kane, Deb M

2016-08-01

Permutation entropy (PE) is commonly used to discriminate complex structure from white noise in a time series. While the PE of white noise is well understood in the long time-series limit, analysis in the general case is currently lacking. Here the expectation value and variance of white-noise PE are derived as functions of the number of ordinal pattern trials, N, and the embedding dimension, D. It is demonstrated that the probability distribution of the white-noise PE converges to a χ^{2} distribution with D!-1 degrees of freedom as N becomes large. It is further demonstrated that the PE variance for an arbitrary time series can be estimated as the variance of a related metric, the Kullback-Leibler entropy (KLE), allowing the qualitative N≫D! condition to be recast as a quantitative estimate of the N required to achieve a desired PE calculation precision. Application of this theory to statistical inference is demonstrated in the case of an experimentally obtained noise series, where the probability of obtaining the observed PE value was calculated assuming a white-noise time series. Standard statistical inference can be used to draw conclusions whether the white-noise null hypothesis can be accepted or rejected. This methodology can be applied to other null hypotheses, such as discriminating whether two time series are generated from different complex system states.

Multiresolution analysis of Bursa Malaysia KLCI time series

Science.gov (United States)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Modelling bursty time series

International Nuclear Information System (INIS)

Vajna, Szabolcs; Kertész, János; Tóth, Bálint

2013-01-01

Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (β) and autocorrelation function (α): α + β = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated. (paper)

Timing calibration and spectral cleaning of LOFAR time series data

NARCIS (Netherlands)

Corstanje, A.; Buitink, S.; Enriquez, J. E.; Falcke, H.; Horandel, J. R.; Krause, M.; Nelles, A.; Rachen, J. P.; Schellart, P.; Scholten, O.; ter Veen, S.; Thoudam, S.; Trinh, T. N. G.

We describe a method for spectral cleaning and timing calibration of short time series data of the voltage in individual radio interferometer receivers. It makes use of phase differences in fast Fourier transform (FFT) spectra across antenna pairs. For strong, localized terrestrial sources these are

Time series momentum and contrarian effects in the Chinese stock market

Science.gov (United States)

Shi, Huai-Long; Zhou, Wei-Xing

2017-10-01

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

Time-Series Analysis: A Cautionary Tale

Science.gov (United States)

Damadeo, Robert

2015-01-01

Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.

Characterizing interdependencies of multiple time series theory and applications

CERN Document Server

Hosoya, Yuzo; Takimoto, Taro; Kinoshita, Ryo

2017-01-01

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an i...

A perturbative approach for enhancing the performance of time series forecasting.

Science.gov (United States)

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cold standby repairable system with working vacations and vacation interruption

Institute of Scientific and Technical Information of China (English)

Baoliang Liu; Lirong Cui; Yanqing Wen

2015-01-01

This paper studies a cold standby repairable system with working vacations and vacation interruption. The repairman’s multiple vacations policy, the working vacations policy and the vacation interruption are considered simultaneously. The lifetime of components fol ows a phase-type (PH) distribution. The repair time in the regular repair period and the working vacation period fol ow other two PH distributions at different rates. For this sys-tem, the vector-valued Markov process governing the system is constructed. We obtain several important performance measures for the system in transient and stationary regimes applying matrix-analytic methods. Final y, a numerical example is given to il ustrate the results obtained.

Drunk driving detection based on classification of multivariate time series.

Science.gov (United States)

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

High voltage series protection of neutral injectors with crossed-field tubes

International Nuclear Information System (INIS)

Hofmann, G.A.; Thomas, D.G.

1976-01-01

High voltage neutral beam injectors for fusion machines require either parallel or series protection schemes to limit fault currents in case of arcing to safe levels. The protection device is usually located between the high voltage supply and beam injector and either crowbars (parallel protection) or disconnects (series protection) the high voltage supply when a fault occurs. Because of its isolating property, series protection is preferred. The Hughes crossed-field tube is uniquely suited for series protection schemes. The tube can conduct 40 A continuously upon application of voltage (approximately 300 V) and a static magnetic field (approximately 100 G). It is also capable of interrupting currents of 1000 A within 10 μs and withstand voltage of more than 120 kV. Experiments were performed to simulate the duty of a crossed-field tube as a series protection element in a neutral injector circuit under fault conditions. Results of on-switching tests under high and low voltage and interruption of fault currents are presented. An example of a possible protection circuit with crossed-field tubes is discussed

Customer interruption cost and results

Energy Technology Data Exchange (ETDEWEB)

Eua-Arporn, B.; Bisarnbutra, S. [Chulalongkorn Univ., Bangkok (Thailand)

1997-12-31

Results of a comprehensive study on short-term direct impacts and consumer interruption costs, incurred as a result of power supply interruption, were discussed. The emphasis was on questionnaire development, general responses and the average customer damage function of some selected sectors. The customer damage function was established for each category of customers (agriculture, industry, mining, wholesale, retail merchandising, residential, etc) as well as for different locations. Results showed that the average customer damage function depended mostly on customer category. Size and location were not significant factors. 5 refs., 7 tabs.

Evaluation of scaling invariance embedded in short time series.

Directory of Open Access Journals (Sweden)

Xue Pan

Full Text Available Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2. Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03 and sharp confidential interval (standard deviation ≤0.05. Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Evaluation of scaling invariance embedded in short time series.

Science.gov (United States)

Pan, Xue; Hou, Lei; Stephen, Mutua; Yang, Huijie; Zhu, Chenping

2014-01-01

Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2). Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03) and sharp confidential interval (standard deviation ≤0.05). Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

Science.gov (United States)

Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

2015-02-01

We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

Geomechanical time series and its singularity spectrum analysis

Czech Academy of Sciences Publication Activity Database

Lyubushin, Alexei A.; Kaláb, Zdeněk; Lednická, Markéta

2012-01-01

Roč. 47, č. 1 (2012), s. 69-77 ISSN 1217-8977 R&D Projects: GA ČR GA105/09/0089 Institutional research plan: CEZ:AV0Z30860518 Keywords : geomechanical time series * singularity spectrum * time series segmentation * laser distance meter Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.347, year: 2012 http://www.akademiai.com/content/88v4027758382225/fulltext.pdf

Interrupting behaviour: Minimizing decision costs via temporal commitment and low-level interrupts

Science.gov (United States)

Dayan, Peter

2018-01-01

Ideal decision-makers should constantly assess all sources of information about opportunities and threats, and be able to redetermine their choices promptly in the face of change. However, perpetual monitoring and reassessment impose inordinate sensing and computational costs, making them impractical for animals and machines alike. The obvious alternative of committing for extended periods of time to limited sensory strategies associated with particular courses of action can be dangerous and wasteful. Here, we explore the intermediate possibility of making provisional temporal commitments whilst admitting interruption based on limited broader observation. We simulate foraging under threat of predation to elucidate the benefits of such a scheme. We relate our results to diseases of distractibility and roving attention, and consider mechanistic substrates such as noradrenergic neuromodulation. PMID:29338004

Interrupting behaviour: Minimizing decision costs via temporal commitment and low-level interrupts.

Science.gov (United States)

Lloyd, Kevin; Dayan, Peter

2018-01-01

Ideal decision-makers should constantly assess all sources of information about opportunities and threats, and be able to redetermine their choices promptly in the face of change. However, perpetual monitoring and reassessment impose inordinate sensing and computational costs, making them impractical for animals and machines alike. The obvious alternative of committing for extended periods of time to limited sensory strategies associated with particular courses of action can be dangerous and wasteful. Here, we explore the intermediate possibility of making provisional temporal commitments whilst admitting interruption based on limited broader observation. We simulate foraging under threat of predation to elucidate the benefits of such a scheme. We relate our results to diseases of distractibility and roving attention, and consider mechanistic substrates such as noradrenergic neuromodulation.

Interrupting behaviour: Minimizing decision costs via temporal commitment and low-level interrupts.

Directory of Open Access Journals (Sweden)

Kevin Lloyd

2018-01-01

Full Text Available Ideal decision-makers should constantly assess all sources of information about opportunities and threats, and be able to redetermine their choices promptly in the face of change. However, perpetual monitoring and reassessment impose inordinate sensing and computational costs, making them impractical for animals and machines alike. The obvious alternative of committing for extended periods of time to limited sensory strategies associated with particular courses of action can be dangerous and wasteful. Here, we explore the intermediate possibility of making provisional temporal commitments whilst admitting interruption based on limited broader observation. We simulate foraging under threat of predation to elucidate the benefits of such a scheme. We relate our results to diseases of distractibility and roving attention, and consider mechanistic substrates such as noradrenergic neuromodulation.

Pseudo-random bit generator based on lag time series

Science.gov (United States)

García-Martínez, M.; Campos-Cantón, E.

2014-12-01

In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

Non-linear forecasting in high-frequency financial time series

Science.gov (United States)

Strozzi, F.; Zaldívar, J. M.

2005-08-01

A new methodology based on state space reconstruction techniques has been developed for trading in financial markets. The methodology has been tested using 18 high-frequency foreign exchange time series. The results are in apparent contradiction with the efficient market hypothesis which states that no profitable information about future movements can be obtained by studying the past prices series. In our (off-line) analysis positive gain may be obtained in all those series. The trading methodology is quite general and may be adapted to other financial time series. Finally, the steps for its on-line application are discussed.

Analysis of JET ELMy time series

International Nuclear Information System (INIS)

Zvejnieks, G.; Kuzovkov, V.N.

2005-01-01

Full text: Achievement of the planned operational regime in the next generation tokamaks (such as ITER) still faces principal problems. One of the main challenges is obtaining the control of edge localized modes (ELMs), which should lead to both long plasma pulse times and reasonable divertor life time. In order to control ELMs the hypothesis was proposed by Degeling [1] that ELMs exhibit features of chaotic dynamics and thus a standard chaos control methods might be applicable. However, our findings which are based on the nonlinear autoregressive (NAR) model contradict this hypothesis for JET ELMy time-series. In turn, it means that ELM behavior is of a relaxation or random type. These conclusions coincide with our previous results obtained for ASDEX Upgrade time series [2]. [1] A.W. Degeling, Y.R. Martin, P.E. Bak, J. B.Lister, and X. Llobet, Plasma Phys. Control. Fusion 43, 1671 (2001). [2] G. Zvejnieks, V.N. Kuzovkov, O. Dumbrajs, A.W. Degeling, W. Suttrop, H. Urano, and H. Zohm, Physics of Plasmas 11, 5658 (2004)

The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

Analysis of time series and size of equivalent sample

International Nuclear Information System (INIS)

Bernal, Nestor; Molina, Alicia; Pabon, Daniel; Martinez, Jorge

2004-01-01

In a meteorological context, a first approach to the modeling of time series is to use models of autoregressive type. This allows one to take into account the meteorological persistence or temporal behavior, thereby identifying the memory of the analyzed process. This article seeks to pre-sent the concept of the size of an equivalent sample, which helps to identify in the data series sub periods with a similar structure. Moreover, in this article we examine the alternative of adjusting the variance of the series, keeping in mind its temporal structure, as well as an adjustment to the covariance of two time series. This article presents two examples, the first one corresponding to seven simulated series with autoregressive structure of first order, and the second corresponding to seven meteorological series of anomalies of the air temperature at the surface in two Colombian regions

Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

Energy Technology Data Exchange (ETDEWEB)

Simmhan, Yogesh; Noor, Muhammad Usman

2013-10-09

Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

Stepping Stones and Creating Futures intervention: shortened interrupted time series evaluation of a behavioural and structural health promotion and violence prevention intervention for young people in informal settlements in Durban, South Africa.

Science.gov (United States)

Jewkes, Rachel; Gibbs, Andrew; Jama-Shai, Nwabisa; Willan, Samantha; Misselhorn, Alison; Mushinga, Mildred; Washington, Laura; Mbatha, Nompumelelo; Skiweyiya, Yandisa

2014-12-29

Gender-based violence and HIV are highly prevalent in the harsh environment of informal settlements and reducing violence here is very challenging. The group intervention Stepping Stones has been shown to reduce men's perpetration of violence in more rural areas, but violence experienced by women in the study was not affected. Economic empowerment interventions with gender training can protect older women from violence, but microloan interventions have proved challenging with young women. We investigated whether combining a broad economic empowerment intervention and Stepping Stones could impact on violence among young men and women. The intervention, Creating Futures, was developed as a new generation of economic empowerment intervention, which enabled livelihood strengthening though helping participants find work or set up a business, and did not give cash or make loans. We piloted Stepping Stones with Creating Futures in two informal settlements of Durban with 232 out of school youth, mostly aged 18-30 and evaluated with a shortened interrupted time series of two baseline surveys and at 28 and 58 weeks post-baseline. 94/110 men and 111/122 women completed the last assessment, 85.5% and 90.2% respectively of those enrolled. To determine trend, we built random effects regression models with each individual as the cluster for each variable, and measured the slope of the line across the time points. Men's mean earnings in the past month increased by 247% from R411 (~$40) to R1015 (~$102, and women's by 278% R 174 (~$17) to R 484 (about $48) (trend test, p < 0.0001). There was a significant reduction in women's experience of the combined measure of physical and/or sexual IPV in the prior three months from 30.3% to 18.9% (p = 0.037). This was not seen for men. However both men and women scored significantly better on gender attitudes and men significantly reduced their controlling practices in their relationship. The prevalence of moderate or severe depression

Forecasting with nonlinear time series models

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...... applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic...... and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...

Nonparametric factor analysis of time series

OpenAIRE

Rodríguez-Poo, Juan M.; Linton, Oliver Bruce

1998-01-01

We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.

Time Series Outlier Detection Based on Sliding Window Prediction

Directory of Open Access Journals (Sweden)

Yufeng Yu

2014-01-01

Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

Metagenomics meets time series analysis: unraveling microbial community dynamics

NARCIS (Netherlands)

Faust, K.; Lahti, L.M.; Gonze, D.; Vos, de W.M.; Raes, J.

2015-01-01

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic

Time series forecasting based on deep extreme learning machine

NARCIS (Netherlands)

Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

2017-01-01

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

False-nearest-neighbors algorithm and noise-corrupted time series

International Nuclear Information System (INIS)

Rhodes, C.; Morari, M.

1997-01-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented. copyright 1997 The American Physical Society

CauseMap: fast inference of causality from complex time series.

Science.gov (United States)

Maher, M Cyrus; Hernandez, Ryan D

2015-01-01

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a

CauseMap: fast inference of causality from complex time series

Directory of Open Access Journals (Sweden)

M. Cyrus Maher

2015-03-01

Full Text Available Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data.Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM, a method for establishing causality from long time series data (≳25 observations. Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens’ Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement

Time domain series system definition and gear set reliability modeling

International Nuclear Information System (INIS)

Xie, Liyang; Wu, Ningxiang; Qian, Wenxue

2016-01-01

Time-dependent multi-configuration is a typical feature for mechanical systems such as gear trains and chain drives. As a series system, a gear train is distinct from a traditional series system, such as a chain, in load transmission path, system-component relationship, system functioning manner, as well as time-dependent system configuration. Firstly, the present paper defines time-domain series system to which the traditional series system reliability model is not adequate. Then, system specific reliability modeling technique is proposed for gear sets, including component (tooth) and subsystem (tooth-pair) load history description, material priori/posterior strength expression, time-dependent and system specific load-strength interference analysis, as well as statistically dependent failure events treatment. Consequently, several system reliability models are developed for gear sets with different tooth numbers in the scenario of tooth root material ultimate tensile strength failure. The application of the models is discussed in the last part, and the differences between the system specific reliability model and the traditional series system reliability model are illustrated by virtue of several numerical examples. - Highlights: • A new type of series system, i.e. time-domain multi-configuration series system is defined, that is of great significance to reliability modeling. • Multi-level statistical analysis based reliability modeling method is presented for gear transmission system. • Several system specific reliability models are established for gear set reliability estimation. • The differences between the traditional series system reliability model and the new model are illustrated.

Nested Interrupt Analysis of Low Cost and High Performance Embedded Systems Using GSPN Framework

Science.gov (United States)

Lin, Cheng-Min

Interrupt service routines are a key technology for embedded systems. In this paper, we introduce the standard approach for using Generalized Stochastic Petri Nets (GSPNs) as a high-level model for generating CTMC Continuous-Time Markov Chains (CTMCs) and then use Markov Reward Models (MRMs) to compute the performance for embedded systems. This framework is employed to analyze two embedded controllers with low cost and high performance, ARM7 and Cortex-M3. Cortex-M3 is designed with a tail-chaining mechanism to improve the performance of ARM7 when a nested interrupt occurs on an embedded controller. The Platform Independent Petri net Editor 2 (PIPE2) tool is used to model and evaluate the controllers in terms of power consumption and interrupt overhead performance. Using numerical results, in spite of the power consumption or interrupt overhead, Cortex-M3 performs better than ARM7.

Track Irregularity Time Series Analysis and Trend Forecasting

Directory of Open Access Journals (Sweden)

Jia Chaolong

2012-01-01

Full Text Available The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM (1,1 is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

PRESEE: an MDL/MML algorithm to time-series stream segmenting.

Science.gov (United States)

Xu, Kaikuo; Jiang, Yexi; Tang, Mingjie; Yuan, Changan; Tang, Changjie

2013-01-01

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.

Time-varying surrogate data to assess nonlinearity in nonstationary time series: application to heart rate variability.

Science.gov (United States)

Faes, Luca; Zhao, He; Chon, Ki H; Nollo, Giandomenico

2009-03-01

We propose a method to extend to time-varying (TV) systems the procedure for generating typical surrogate time series, in order to test the presence of nonlinear dynamics in potentially nonstationary signals. The method is based on fitting a TV autoregressive (AR) model to the original series and then regressing the model coefficients with random replacements of the model residuals to generate TV AR surrogate series. The proposed surrogate series were used in combination with a TV sample entropy (SE) discriminating statistic to assess nonlinearity in both simulated and experimental time series, in comparison with traditional time-invariant (TIV) surrogates combined with the TIV SE discriminating statistic. Analysis of simulated time series showed that using TIV surrogates, linear nonstationary time series may be erroneously regarded as nonlinear and weak TV nonlinearities may remain unrevealed, while the use of TV AR surrogates markedly increases the probability of a correct interpretation. Application to short (500 beats) heart rate variability (HRV) time series recorded at rest (R), after head-up tilt (T), and during paced breathing (PB) showed: 1) modifications of the SE statistic that were well interpretable with the known cardiovascular physiology; 2) significant contribution of nonlinear dynamics to HRV in all conditions, with significant increase during PB at 0.2 Hz respiration rate; and 3) a disagreement between TV AR surrogates and TIV surrogates in about a quarter of the series, suggesting that nonstationarity may affect HRV recordings and bias the outcome of the traditional surrogate-based nonlinearity test.

Local normalization: Uncovering correlations in non-stationary financial time series

Science.gov (United States)

Schäfer, Rudi; Guhr, Thomas

2010-09-01

The measurement of correlations between financial time series is of vital importance for risk management. In this paper we address an estimation error that stems from the non-stationarity of the time series. We put forward a method to rid the time series of local trends and variable volatility, while preserving cross-correlations. We test this method in a Monte Carlo simulation, and apply it to empirical data for the S&P 500 stocks.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Science.gov (United States)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

The impact of harm reduction programs and police interventions on the number of syringes collected from public spaces. A time series analysis in Barcelona, 2004-2014.

Science.gov (United States)

Espelt, A; Villalbí, J R; Bosque-Prous, M; Parés-Badell, O; Mari-Dell'Olmo, M; Brugal, M T

2017-12-01

To estimate the effect of opening two services for people who use drugs and three police interventions on the number of discarded syringes collected from public spaces in Barcelona between 2004 and 2014. We conducted an interrupted time-series analysis of the monthly number of syringes collected from public spaces during this period. The dependent variable was the number of syringes collected per month. The main independent variables were month and five dummy variables (the opening of two facilities with safe consumption rooms, and three police interventions). To examine which interventions affected the number of syringes collected, we performed an interrupted time-series analysis using a quasi-Poisson regression model, obtaining relative risks (RR) and 95% confidence intervals (CIs). The number of syringes collected per month in Barcelona decreased from 13,800 in 2004 to 1655 in 2014 after several interventions. For example, following the closure of an open drug scene in District A of the city, we observed a decreasing trend in the number of syringes collected [RR=0.88 (95% CI: 0.82-0.95)], but an increasing trend in the remaining districts [RR=1.11 (95% CI: 1.05-1.17) and 1.08 (95% CI: 0.99-1.18) for districts B and C, respectively]. Following the opening of a harm reduction facility in District C, we observed an initial increase in the number collected in this district [RR=2.72 (95% CI: 1.57-4.71)] and stabilization of the trend thereafter [RR=0.97 (95% CI: 0.91-1.03)]. The overall number of discarded syringes collected from public spaces has decreased consistently in parallel with a combination of police interventions and the opening of harm reduction facilities. Copyright © 2017 Elsevier B.V. All rights reserved.

Parameterizing unconditional skewness in models for financial time series

DEFF Research Database (Denmark)

He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo

In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate...

Self-organising mixture autoregressive model for non-stationary time series modelling.

Science.gov (United States)

Ni, He; Yin, Hujun

2008-12-01

Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

Factors defining face-to-face interruptions in the office environment

NARCIS (Netherlands)

Matysiak, A.J.; Markopoulos, P.

2006-01-01

This paper presents an on-going investigation on interruptions in the office caused by face-to-face interactions between knowledge workers. The study aims to identify opportunities for interactive solutions that will support both, the interrupters and the interrupted. The study involves contextual

Microsurgical Performance After Sleep Interruption: A NeuroTouch Simulator Study.

Science.gov (United States)

Micko, Alexander; Knopp, Karoline; Knosp, Engelbert; Wolfsberger, Stefan

2017-10-01

In times of the ubiquitous debate about doctors' working hour restrictions, it is still questionable if the physician's performance is impaired by high work load and long shifts. In this study, we evaluated the impact of sleep interruption on neurosurgical performance. Ten medical students and 10 neurosurgical residents were tested on the virtual-reality simulator NeuroTouch by performing an identical microsurgical task, well rested (baseline test), and after sleep interruption at night (stress test). Deviation of total score, timing, and excessive force on tissue were evaluated. In addition, vital parameters and self-assessment were analyzed. After sleep interruption, total performance score increased significantly (45.1 vs. 48.7, baseline vs. stress test, P = 0.048) while timing remained stable (10.1 vs. 10.4 minutes for baseline vs. stress test, P > 0.05) for both students and residents. Excessive force decreased in both groups during the stress test for the nondominant hand (P = 0.05). For the dominant hand, an increase of excessive force was encountered in the group of residents (P = 0.05). In contrast to their results, participants of both groups assessed their performance worse during the stress test. In our study, we found an increase of neurosurgical simulator performance in neurosurgical residents and medical students under simulated night shift conditions. Further, microsurgical dexterity remained unchanged. Based on our results and the data in the available literature, we cannot confirm that working hour restrictions will have a positive effect on neurosurgical performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of Data Interruption in an LTE Highway Scenario with Dual Connectivity

DEFF Research Database (Denmark)

Gimenez, Lucas Chavarria; Michaelsen, Per-Henrik; Pedersen, Klaus I.

2016-01-01

This study evaluates whether last versions of Long Term Evolution with dual connectivity are able to support the latency and reliability requirements for the upcoming vehicular use-cases and time-critical applications. Data interruption times during handovers and cell management operations are ev...

The Prediction of Teacher Turnover Employing Time Series Analysis.

Science.gov (United States)

Costa, Crist H.

The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

Repeat interruptions in spinocerebellar ataxia type 10 expansions are strongly associated with epileptic seizures

Science.gov (United States)

McFarland, Karen N.; Liu, Jilin; Landrian, Ivette; Zeng, Desmond; Raskin, Salmo; Moscovich, Mariana; Gatto, Emilia M.; Ochoa, Adriana; Teive, Hélio A. G.; Rasmussen, Astrid; Ashizawa, Tetsuo

2014-01-01

Spinocerebellar ataxia type 10 (SCA10), an autosomal dominant neurodegenerative disorder, is the result of a non-coding, pentanucleotide repeat expansion within intron 9 of the Ataxin 10 gene. SCA10 patients present with pure cerebellar ataxia; yet, some families also have a high incidence of epilepsy. SCA10 expansions containing penta- and heptanucleotide interruption motifs, termed “ATCCT interruptions,” experience large contractions during germline transmission, particularly in paternal lineages. At the same time, these alleles confer an earlier age at onset which contradicts traditional rules of genetic anticipation in repeat expansions. Previously, ATCCT interruptions have been associated with a higher prevalence of epileptic seizures in one Mexican-American SCA10 family. In a large cohort of SCA10 families, we analyzed whether ATCCT interruptions confers a greater risk for developing seizures in these families. Notably, we find that the presence of repeat interruptions within the SCA10 expansion confers a 6.3-fold increase in the risk of an SCA10 patient developing epilepsy (6.2-fold when considering patients of Mexican ancestry only) and a 13.7-fold increase in having a positive family history of epilepsy (10.5-fold when considering patients of Mexican ancestry only). We conclude that the presence of repeat interruptions in SCA10 repeat expansion indicates a significant risk for the epilepsy phenotype and should be considered during genetic counseling. PMID:24318420

When daily planning improves employee performance: The importance of planning type, engagement, and interruptions.

Science.gov (United States)

Parke, Michael R; Weinhardt, Justin M; Brodsky, Andrew; Tangirala, Subrahmaniam; DeVoe, Sanford E

2018-03-01

Does planning for a particular workday help employees perform better than on other days they fail to plan? We investigate this question by identifying 2 distinct types of daily work planning to explain why and when planning improves employees' daily performance. The first type is time management planning (TMP)-creating task lists, prioritizing tasks, and determining how and when to perform them. We propose that TMP enhances employees' performance by increasing their work engagement, but that these positive effects are weakened when employees face many interruptions in their day. The second type is contingent planning (CP) in which employees anticipate possible interruptions in their work and plan for them. We propose that CP helps employees stay engaged and perform well despite frequent interruptions. We investigate these hypotheses using a 2-week experience-sampling study. Our findings indicate that TMP's positive effects are conditioned upon the amount of interruptions, but CP has positive effects that are not influenced by the level of interruptions. Through this study, we help inform workers of the different planning methods they can use to increase their daily motivation and performance in dynamic work environments. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Stacked Heterogeneous Neural Networks for Time Series Forecasting

Directory of Open Access Journals (Sweden)

Florin Leon

2010-01-01

Full Text Available A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied.

Chaotic time series prediction: From one to another

International Nuclear Information System (INIS)

Zhao Pengfei; Xing Lei; Yu Jun

2009-01-01

In this Letter, a new local linear prediction model is proposed to predict a chaotic time series of a component x(t) by using the chaotic time series of another component y(t) in the same system with x(t). Our approach is based on the phase space reconstruction coming from the Takens embedding theorem. To illustrate our results, we present an example of Lorenz system and compare with the performance of the original local linear prediction model.

Task Interruption: Resumption Lag and the Role of Cues

National Research Council Canada - National Science Library

Altmann, Erik M; Trafton, J. G

2004-01-01

...), indicating a substantial disruptive effect. To probe the nature of the disruption, they examined the role of external cues associated with the interrupted task and found that cues available immediately before an interruption facilitate performance immediately afterwards, thus reducing the resumption lag. This "cue-availability" effect suggests that people deploy preparatory perceptual and memory processes, apparently spontaneously, to mitigate the disruptive effects of task interruption.

Grammar-based feature generation for time-series prediction

CERN Document Server

De Silva, Anthony Mihirana

2015-01-01

This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method ...

HIV models for treatment interruption: Adaptation and comparison

Science.gov (United States)

Hillmann, Andreas; Crane, Martin; Ruskin, Heather J.

2017-10-01

In recent years, Antiretroviral Therapy (ART) has become commonplace for treating HIV infections, although a cure remains elusive, given reservoirs of replicating latently-infected cells, which are resistant to normal treatment regimes. Treatment interruptions, whether ad hoc or structured, are known to cause a rapid increase in viral production to detectable levels, but numerous clinical trials remain inconclusive on the dangers inherent in this resurgence. In consequence, interest in examining interruption strategies has recently been rekindled. This overview considers modelling approaches, which have been used to explore the issue of treatment interruption. We highlight their purpose and the formalisms employed and examine ways in which clinical data have been used. Implementation of selected models is demonstrated, illustrative examples provided and model performance compared for these cases. Possible extensions to bottom-up modelling techniques for treatment interruptions are briefly discussed.

Forecasting autoregressive time series under changing persistence

DEFF Research Database (Denmark)

Kruse, Robinson

Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Science.gov (United States)

Che, Zhengping; Purushotham, Sanjay; Cho, Kyunghyun; Sontag, David; Liu, Yan

2018-04-17

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.

Conditional time series forecasting with convolutional neural networks

NARCIS (Netherlands)

A. Borovykh (Anastasia); S.M. Bohte (Sander); C.W. Oosterlee (Cornelis)

2017-01-01

textabstractForecasting financial time series using past observations has been a significant topic of interest. While temporal relationships in the data exist, they are difficult to analyze and predict accurately due to the non-linear trends and noise present in the series. We propose to learn these

Time Series Analysis of Wheat Futures Reward in China

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

Different from the fact that the main researches are focused on single futures contract and lack of the comparison of different periods, this paper described the statistical characteristics of wheat futures reward time series of Zhengzhou Commodity Exchange in recent three years. Besides the basic statistic analysis, the paper used the GARCH and EGARCH model to describe the time series which had the ARCH effect and analyzed the persistence of volatility shocks and the leverage effect. The results showed that compared with that of normal one,wheat futures reward series were abnormality, leptokurtic and thick tail distribution. The study also found that two-part of the reward series had no autocorrelation. Among the six correlative series, three ones presented the ARCH effect. By using of the Auto-regressive Distributed Lag Model, GARCH model and EGARCH model, the paper demonstrates the persistence of volatility shocks and the leverage effect on the wheat futures reward time series. The results reveal that on the one hand, the statistical characteristics of the wheat futures reward are similar to the aboard mature futures market as a whole. But on the other hand, the results reflect some shortages such as the immatureness and the over-control by the government in the Chinese future market.

forecasting with nonlinear time series model: a monte-carlo

African Journals Online (AJOL)

PUBLICATIONS1

erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.

Time series analysis of temporal networks

Science.gov (United States)

Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

2016-01-01

A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

Cerebrospinal fluid signs of neuronal damage after antiretroviral treatment interruption in HIV-1 infection

Directory of Open Access Journals (Sweden)

Deeks Steven G

2005-08-01

Full Text Available Abstract Background The neurofilament is a major structural component of myelinated axons. Increased cerebrospinal fluid (CSF concentrations of the light chain of the neurofilament protein (NFL can serve as a sensitive indicator of central nervous system (CNS injury. To assess whether interrupting antiretroviral treatment of HIV infection might have a deleterious effect on the CNS, we measured NFL levels in HIV-infected subjects interrupting therapy. We identified subjects who had CSF HIV RNA concentrations below 50 copies/mL at the time combination antiretroviral therapy was interrupted, and for whom CSF samples were available before and after the interruption. Results A total of 8 subjects were studied. The median (range CSF NFL level at baseline was Conclusion These findings suggest that resurgence of active HIV replication may result in measurable, albeit subclinical, CNS injury. Further studies are needed to define the frequency and pathobiological importance of the increase in CSF NFL.

The Hierarchical Spectral Merger Algorithm: A New Time Series Clustering Procedure

KAUST Repository

Euá n, Carolina; Ombao, Hernando; Ortega, Joaquí n

2018-01-01

We present a new method for time series clustering which we call the Hierarchical Spectral Merger (HSM) method. This procedure is based on the spectral theory of time series and identifies series that share similar oscillations or waveforms

Notes on economic time series analysis system theoretic perspectives

CERN Document Server

Aoki, Masanao

1983-01-01

In seminars and graduate level courses I have had several opportunities to discuss modeling and analysis of time series with economists and economic graduate students during the past several years. These experiences made me aware of a gap between what economic graduate students are taught about vector-valued time series and what is available in recent system literature. Wishing to fill or narrow the gap that I suspect is more widely spread than my personal experiences indicate, I have written these notes to augment and reor­ ganize materials I have given in these courses and seminars. I have endeavored to present, in as much a self-contained way as practicable, a body of results and techniques in system theory that I judge to be relevant and useful to economists interested in using time series in their research. I have essentially acted as an intermediary and interpreter of system theoretic results and perspectives in time series by filtering out non-essential details, and presenting coherent accounts of wha...

Tests of vacuum interrupters for the Tokamak Fusion Test Reactor

International Nuclear Information System (INIS)

Warren, R.; Parsons, M.; Honig, E.; Lindsay, J.

1979-04-01

The Tokamak Fusion Test Reactor (TFTR) project at Princeton University requires the insertion of a resistor in an excited ohmic-heating coil circuit to produce a plasma initiation pulse (PIP). It is expected that the maximum duty for the switching system will be an interruption of 24 kA with an associated recovery voltage of 25 kV. Vacuum interrupters were selected as the most economical means to satisfy these requirements. However, it was felt that some testing of available systems should be performed to determine their reliability under these conditions. Two interrupter systems were tested for over 1000 interruptions each at 24 kA and 25 kV. One system employed special Westinghouse type WL-33552 interrupters in a circuit designed by LASL. This circuit used a commercially available actuator and a minimum size counterpulse bank and saturable reactor. The other used Toshiba type VGB2-D20 interrupters actuated by a Toshiba mechanism in a Toshiba circuit using a larger counterpulse bank and saturable reactor

Dynamical analysis and visualization of tornadoes time series.

Directory of Open Access Journals (Sweden)

António M Lopes

Full Text Available In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Dynamical analysis and visualization of tornadoes time series.

Science.gov (United States)

Lopes, António M; Tenreiro Machado, J A

2015-01-01

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

"Observation Obscurer" - Time Series Viewer, Editor and Processor

Science.gov (United States)

Andronov, I. L.

The program is described, which contains a set of subroutines suitable for East viewing and interactive filtering and processing of regularly and irregularly spaced time series. Being a 32-bit DOS application, it may be used as a default fast viewer/editor of time series in any compute shell ("commander") or in Windows. It allows to view the data in the "time" or "phase" mode, to remove ("obscure") or filter outstanding bad points; to make scale transformations and smoothing using few methods (e.g. mean with phase binning, determination of the statistically opti- mal number of phase bins; "running parabola" (Andronov, 1997, As. Ap. Suppl, 125, 207) fit and to make time series analysis using some methods, e.g. correlation, autocorrelation and histogram analysis: determination of extrema etc. Some features have been developed specially for variable star observers, e.g. the barycentric correction, the creation and fast analysis of "OC" diagrams etc. The manual for "hot keys" is presented. The computer code was compiled with a 32-bit Free Pascal (www.freepascal.org).

Modelling road accidents: An approach using structural time series

Science.gov (United States)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Change in non-alcoholic beverage sales following a 10-pence levy on sugar-sweetened beverages within a national chain of restaurants in the UK: interrupted time series analysis of a natural experiment.

Science.gov (United States)

Cornelsen, Laura; Mytton, Oliver T; Adams, Jean; Gasparrini, Antonio; Iskander, Dalia; Knai, Cecile; Petticrew, Mark; Scott, Courtney; Smith, Richard; Thompson, Claire; White, Martin; Cummins, Steven

2017-11-01

This study evaluates changes in sales of non-alcoholic beverages in Jamie's Italian, a national chain of commercial restaurants in the UK, following the introduction of a £0.10 per-beverage levy on sugar-sweetened beverages (SSBs) and supporting activity including beverage menu redesign, new products and establishment of a children's health fund from levy proceeds. We used an interrupted time series design to quantify changes in sales of non-alcoholic beverages 12 weeks and 6 months after implementation of the levy, using itemised electronic point of sale data. Main outcomes were number of SSBs and other non-alcoholic beverages sold per customer. Linear regression and multilevel random effects models, adjusting for seasonality and clustering, were used to investigate changes in SSB sales across all restaurants (n=37) and by tertiles of baseline restaurant SSB sales per customer. Compared with the prelevy period, the number of SSBs sold per customer declined by 11.0% (-17.3% to -4.3%) at 12 weeks and 9.3% (-15.2% to -3.2%) at 6 months. For non-levied beverages, sales per customer of children's fruit juice declined by 34.7% (-55.3% to -4.3%) at 12 weeks and 9.9% (-16.8% to -2.4%) at 6 months. At 6 months, sales per customer of fruit juice increased by 21.8% (14.0% to 30.2%) but sales of diet cola (-7.3%; -11.7% to -2.8%) and bottled waters (-6.5%; -11.0% to -1.7%) declined. Changes in sales were only observed in restaurants in the medium and high tertiles of baseline SSB sales per customer. Introduction of a £0.10 levy on SSBs alongside complementary activities is associated with declines in SSB sales per customer in the short and medium term, particularly in restaurants with higher baseline sales of SSBs. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

Science.gov (United States)

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Chemoradiotherapy in patients with anal cancer: Impact of length of unplanned treatment interruption on outcome

Energy Technology Data Exchange (ETDEWEB)

Meyer, Andreas; Meier Zu Eissen, Juergen; Karstens, Johann H.; Bremer, Michael [Medical School Hannover (Germany). Dept. of Radiation Oncology

2006-09-15

The aim of this retrospective analysis was to evaluate feasibility and effectiveness of definitive chemoradiotherapy without split-course technique in anal cancer patients. From 1993 to 2003, 81 patients were treated; 13 were excluded due to various chemotherapeutic regimes, thus 68 patients were analysed. In case of acute grade 3 toxicities, treatment was halted until improvement or resolution independent of dose. Short interruption was defined as completing treatment without exceeding eight cumulative treatment days beyond scheduled plan, other patients were considered to have had prolonged interruption. Median follow-up was 46 months. Median overall treatment time was 53 days corresponding to an interruption of eight cumulative treatment days. Thirty-five patients (51%) had treatment interruption of <8 days. No acute grade 4 toxicities were observed; one fatality occurred during treatment due to ileus-like symptoms according to acute grade 5 toxicity. Comparing patients with short vs. prolonged interruption 5-year actuarial rates for local control were 85% vs. 81% (p{approx}0.605) and for colostomy-free survival 85% vs. 87% (p{approx}0.762), respectively. Chemoradiotherapy with short individualised treatment interruptions seems to be feasible with acceptable acute or late toxicities. Treatment is highly effective in terms of local control and colostomy-free survival.

Time series patterns and language support in DBMS

Science.gov (United States)

Telnarova, Zdenka

2017-07-01

This contribution is focused on pattern type Time Series as a rich in semantics representation of data. Some example of implementation of this pattern type in traditional Data Base Management Systems is briefly presented. There are many approaches how to manipulate with patterns and query patterns. Crucial issue can be seen in systematic approach to pattern management and specific pattern query language which takes into consideration semantics of patterns. Query language SQL-TS for manipulating with patterns is shown on Time Series data.

Two-fractal overlap time series: Earthquakes and market crashes

Indian Academy of Sciences (India)

velocity over the other and time series of stock prices. An anticipation method for some of the crashes have been proposed here, based on these observations. Keywords. Cantor set; time series; earthquake; market crash. PACS Nos 05.00; 02.50.-r; 64.60; 89.65.Gh; 95.75.Wx. 1. Introduction. Capturing dynamical patterns of ...

Evaluating the Impact of Criminalizing Drunk Driving on Road-Traffic Injuries in Guangzhou, China: A Time-Series Study.

Science.gov (United States)

Zhao, Ang; Chen, Renjie; Qi, Yongqing; Chen, Ailan; Chen, Xinyu; Liang, Zijing; Ye, Jianjun; Liang, Qing; Guo, Duanqiang; Li, Wanglin; Li, Shuangming; Kan, Haidong

2016-08-05

Road-traffic injury (RTI) is a major public-health concern worldwide. However, the effectiveness of laws criminalizing drunk driving on the improvement of road safety in China is not known. We collected daily aggregate data on RTIs from the Guangzhou First-Aid Service Command Center from 2009 to 2012. We performed an interrupted time-series analysis to evaluate the change in daily RTIs before (January 1, 2009, to April 30, 2011) and after (May 1, 2011, to December 31, 2012) the criminalization of drunk driving. We evaluated the impact of the intervention on RTIs using the overdispersed generalized additive model after adjusting for temporal trends, seasonality, day of the week, and holidays. Daytime/Nighttime RTIs, alcoholism, and non-traffic injuries were analyzed as comparison groups using the same model. From January 1, 2009, to December 31, 2012, we identified a total of 54 887 RTIs. The standardized daily number of RTIs was almost stable in the pre-intervention period but decreased gradually in the post-intervention period. After the intervention, the standardized daily RTIs decreased 9.6% (95% confidence interval [CI], 6.5%-12.8%). There were similar decreases for the daily daytime and nighttime RTIs. In contrast, the standardized daily cases of alcoholism increased 38.8% (95% CI, 35.1%-42.4%), and daily non-traffic injuries increased 3.6% (95% CI, 1.4%-5.8%). This time-series study provides scientific evidence suggesting that the criminalization of drunk driving from May 1, 2011, may have led to moderate reductions in RTIs in Guangzhou, China.

Effects of interruption of irradiation on Harwell Red Perspex (PMMA)

International Nuclear Information System (INIS)

Khayet Tebourbi, Mohamed anouar abdelaziz

2010-01-01

Harwell Red Perspex PMMA (Polymethylmethacrylate) is a dosimeter very much used in the industrial treatments by Radiations ionizing. The purpose of this work is to test the response of this dosimeter for radiation processes having undergone one or more interruptions. This experimental study based on the development of a factorial experimental design on two levels showed that the response of this dosimeter increases for the interrupted treatments. The value of the estimated amount of response increase is all the more significant as the temperature during the interruption is high. Also it made possible to determine a mathematical model binding the value of the amount posted to the factors of influence: Temperature, target amount, a number of interruptions and duration of each interruption.

Nonlinear time series analysis with R

CERN Document Server

Huffaker, Ray; Rosa, Rodolfo

2017-01-01

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjec...

InSAR Deformation Time Series Processed On-Demand in the Cloud

Science.gov (United States)

Horn, W. B.; Weeden, R.; Dimarchi, H.; Arko, S. A.; Hogenson, K.

2017-12-01

During this past year, ASF has developed a cloud-based on-demand processing system known as HyP3 (http://hyp3.asf.alaska.edu/), the Hybrid Pluggable Processing Pipeline, for Synthetic Aperture Radar (SAR) data. The system makes it easy for a user who doesn't have the time or inclination to install and use complex SAR processing software to leverage SAR data in their research or operations. One such processing algorithm is generation of a deformation time series product, which is a series of images representing ground displacements over time, which can be computed using a time series of interferometric SAR (InSAR) products. The set of software tools necessary to generate this useful product are difficult to install, configure, and use. Moreover, for a long time series with many images, the processing of just the interferograms can take days. Principally built by three undergraduate students at the ASF DAAC, the deformation time series processing relies the new Amazon Batch service, which enables processing of jobs with complex interconnected dependencies in a straightforward and efficient manner. In the case of generating a deformation time series product from a stack of single-look complex SAR images, the system uses Batch to serialize the up-front processing, interferogram generation, optional tropospheric correction, and deformation time series generation. The most time consuming portion is the interferogram generation, because even for a fairly small stack of images many interferograms need to be processed. By using AWS Batch, the interferograms are all generated in parallel; the entire process completes in hours rather than days. Additionally, the individual interferograms are saved in Amazon's cloud storage, so that when new data is acquired in the stack, an updated time series product can be generated with minimal addiitonal processing. This presentation will focus on the development techniques and enabling technologies that were used in developing the time

Instant Messaging Usage and Interruptions in the Workplace

Directory of Open Access Journals (Sweden)

Hui‐Jung Chang

2014-12-01

Full Text Available The goal of the present study is to explore IM interruption by relating it to media choices and purposes of IM use in the workplace. Two major media choice concepts were: media richness and social influence; while four purposes of IM use were: organization work, knowledge work, socializing, and boundary spanning activities. Data (N = 283 were collected via a combination of convenience and snowball sampling of “computer‐using workers” in Taiwan, based on the Standard Occupational Classification system published by the Taiwan government. Results indicated that media choice works better than purpose of IM use to explain IM interruption. Among them, social influence was the best predictor to IM interruption in the workplace. In addition, instant feedback and personalization provided by IM, and IM usage for the purposes of knowledge work and socializing, also relate to IM interruption in the workplace.

Vector bilinear autoregressive time series model and its superiority ...

African Journals Online (AJOL)

In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.

25 years of time series forecasting

NARCIS (Netherlands)

de Gooijer, J.G.; Hyndman, R.J.

2006-01-01

We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985 and International Journal of Forecasting 1985-2005). During