The Spy in Early America: The Emergence of a Genre

National Research Council Canada - National Science Library

Weir, Alison

1998-01-01

... after the Revolution through the antebellum period. By examining the paradoxical figure of the heroic spy, the dissertation explores how the spy story emerged as the adventure tale of the Revolution, the spy became a potential hero, and how the...

Seeing is believing -- SpyJack system investigates field problems

International Nuclear Information System (INIS)

O'Meara, D.

1998-01-01

The SpyJack Remote Monitoring System, a stand-alone, single-site remote well monitoring system that works off its own embedded controller is described. The supervisory control and data acquisition (SCADA) unit, developed by CORE Technologies Inc., has an integrated voice/alarm system, monitoring sensors installed at remote well sites continuously 24 hours a day with a digital camera, with 300 degrees of visual confirmation capability. It can also be used as a surveillance tool against terrorism as has been shown in the case of some recent debilitating attacks on remote wells in Alberta. SpyJack monitors flow rates and pressures, pumpjack stroke rates, sucker rod temperature, load cell and engine shut-off relay, as well as ambient temperature, tank levels, and motion detection. At the office, SpyJack Well Management Interface uses nine drop-down screens to take users through the remote monitoring and control of each site. The screens include production sensors, line sensors, dyno chart, environmental/safety sensors and sensor information and set up. Currently under development is the unsolicited dial-out capability. When implemented the system will send out information at pre-determined times, such as every four hours, or at specific times during the day. Compressing images to enable speedier downloading is also being planned

Positron astronomy with SPI/INTEGRAL

International Nuclear Information System (INIS)

Weidenspointner, G.; Diehl, R.; Strong, A.; Weidenspointner, G.; Skinner, G.K.; Skinner, G.K.; Jean, P.; Knoedlseder, J.; Von Ballmoos, P.; Cordier, B.; Schanne, S.; Winkler, C.

2008-01-01

We provide an overview of positron astronomy results that have been obtained using the INTEGRAL spectrometer SPI, and discuss their implications for the still mysterious origin of positrons in our Galaxy. It has long been known that the 511 keV positron annihilation emission is strongest from the central region of our Galaxy. Recently, it has been discovered with the SPI spectrometer that the weaker 511 keV line emission from the inner Galactic disk appears to be asymmetric, with the emission to the west of the Galactic center being about twice as strong than that to the east. This distribution of positron annihilation resembles that of low mass X-ray binaries as observed with the INTEGRAL imager IBIS at hard X-ray energies, suggesting that these systems could provide a significant portion of the positrons in our Galaxy. In addition, the spectrometer SPI has permitted unprecedented spectroscopy of annihilation radiation from the bulge and disk regions of the Galaxy, which commences to yield important insights into the conditions of the medium in which the positrons annihilate. (authors)

iSpy: a powerful and lightweight event display

Science.gov (United States)

Alverson, G.; Eulisse, G.; McCauley, T.; Taylor, L.

2012-12-01

iSpy is a general-purpose event data and detector visualization program that was developed as an event display for the CMS experiment at the LHC and has seen use by the general public and teachers and students in the context of education and outreach. Central to the iSpy design philosophy is ease of installation, use, and extensibility. The application itself uses the open-access packages Qt4 and Open Inventor and is distributed either as a fully-bound executable or a standard installer package: one can simply download and double-click to begin. Mac OSX, Linux, and Windows are supported. iSpy renders the standard 2D, 3D, and tabular views, and the architecture allows for a generic approach to production of new views and projections. iSpy reads and displays data in the ig format: event information is written in compressed JSON format files designed for distribution over a network. This format is easily extensible and makes the iSpy client indifferent to the original input data source. The ig format is the one used for release of approved CMS data to the public.

iSpy: a powerful and lightweight event display

International Nuclear Information System (INIS)

Alverson, G; Eulisse, G; McCauley, T; Taylor, L

2012-01-01

iSpy is a general-purpose event data and detector visualization program that was developed as an event display for the CMS experiment at the LHC and has seen use by the general public and teachers and students in the context of education and outreach. Central to the iSpy design philosophy is ease of installation, use, and extensibility. The application itself uses the open-access packages Qt4 and Open Inventor and is distributed either as a fully-bound executable or a standard installer package: one can simply download and double-click to begin. Mac OSX, Linux, and Windows are supported. iSpy renders the standard 2D, 3D, and tabular views, and the architecture allows for a generic approach to production of new views and projections. iSpy reads and displays data in the ig format: event information is written in compressed JSON format files designed for distribution over a network. This format is easily extensible and makes the iSpy client indifferent to the original input data source. The ig format is the one used for release of approved CMS data to the public.

Macrophage-specific gene functions in Spi1-directed innate immunity

NARCIS (Netherlands)

Zakrzewska, Anna; Cui, Chao; Stockhammer, Oliver W.; Benard, Erica L.; Spaink, Herman P.; Meijer, Annemarie H.

2010-01-01

The Spi1/Pu.1 transcription factor plays a crucial role in myeloid cell development in vertebrates. Despite extensive studies of Spi1, the controlled gene group remains largely unknown. To identify genes dependent on Spi1, we used a microarray strategy using a knockdown approach in zebrafish embryos

Structural basis of divergent cyclin-dependent kinase activation by Spy1/RINGO proteins

Energy Technology Data Exchange (ETDEWEB)

McGrath, Denise A.; Fifield, Bre-Anne; Marceau, Aimee H.; Tripathi, Sarvind; Porter, Lisa A.; Rubin, Seth M. (UCSC); (Windsor)

2017-06-30

Cyclin-dependent kinases (Cdks) are principal drivers of cell division and are an important therapeutic target to inhibit aberrant proliferation. Cdk enzymatic activity is tightly controlled through cyclin interactions, posttranslational modifications, and binding of inhibitors such as the p27 tumor suppressor protein. Spy1/RINGO (Spy1) proteins bind and activate Cdk but are resistant to canonical regulatory mechanisms that establish cell-cycle checkpoints. Cancer cells exploit Spy1 to stimulate proliferation through inappropriate activation of Cdks, yet the mechanism is unknown. We have determined crystal structures of the Cdk2-Spy1 and p27-Cdk2-Spy1 complexes that reveal how Spy1 activates Cdk. We find that Spy1 confers structural changes to Cdk2 that obviate the requirement of Cdk activation loop phosphorylation. Spy1 lacks the cyclin-binding site that mediates p27 and substrate affinity, explaining why Cdk-Spy1 is poorly inhibited by p27 and lacks specificity for substrates with cyclin-docking sites. We identify mutations in Spy1 that ablate its ability to activate Cdk2 and to proliferate cells. Our structural description of Spy1 provides important mechanistic insights that may be utilized for targeting upregulated Spy1 in cancer.

How Do Artifact Models Help Direct SPI Projects?

DEFF Research Database (Denmark)

Kuhrmann, Marco; Richardson, Ita

2015-01-01

To overcome shortcomings associated with software process improvement (SPI), we previously recommended that process engineers focus on the artifacts to be developed in SPI projects. These artifacts should define desired outcomes, rather than specific methods. During this prior research, we develo...

A framework for plasticity implementation on the SpiNNaker neural architecture.

Science.gov (United States)

Galluppi, Francesco; Lagorce, Xavier; Stromatias, Evangelos; Pfeiffer, Michael; Plana, Luis A; Furber, Steve B; Benosman, Ryad B

2014-01-01

Many of the precise biological mechanisms of synaptic plasticity remain elusive, but simulations of neural networks have greatly enhanced our understanding of how specific global functions arise from the massively parallel computation of neurons and local Hebbian or spike-timing dependent plasticity rules. For simulating large portions of neural tissue, this has created an increasingly strong need for large scale simulations of plastic neural networks on special purpose hardware platforms, because synaptic transmissions and updates are badly matched to computing style supported by current architectures. Because of the great diversity of biological plasticity phenomena and the corresponding diversity of models, there is a great need for testing various hypotheses about plasticity before committing to one hardware implementation. Here we present a novel framework for investigating different plasticity approaches on the SpiNNaker distributed digital neural simulation platform. The key innovation of the proposed architecture is to exploit the reconfigurability of the ARM processors inside SpiNNaker, dedicating a subset of them exclusively to process synaptic plasticity updates, while the rest perform the usual neural and synaptic simulations. We demonstrate the flexibility of the proposed approach by showing the implementation of a variety of spike- and rate-based learning rules, including standard Spike-Timing dependent plasticity (STDP), voltage-dependent STDP, and the rate-based BCM rule. We analyze their performance and validate them by running classical learning experiments in real time on a 4-chip SpiNNaker board. The result is an efficient, modular, flexible and scalable framework, which provides a valuable tool for the fast and easy exploration of learning models of very different kinds on the parallel and reconfigurable SpiNNaker system.

A four channel time-to-digital converter ASIC with in-built calibration and SPI interface

International Nuclear Information System (INIS)

Hari Prasad, K.; Sukhwani, Menka; Saxena, Pooja; Chandratre, V.B.; Pithawa, C.K.

2014-01-01

A design of high resolution, wide dynamic range Time-to-Digital Converter (TDC) ASIC, implemented in 0.35 µm commercial CMOS technology is presented. The ASIC features four channel TDC with an in-built calibration and Serial Peripheral Interconnect (SPI) slave interface. The TDC is based on the vernier ring oscillator method in order to achieve both high resolution and wide dynamic range. This TDC ASIC is tested and found to have resolution of 127 ps (LSB), dynamic range of 1.8 µs and precision (σ) of 74 ps. The measured values of differential non-linearity (DNL) and integral non-linearity (INL) are 350 ps and 300 ps respectively

The Application of Box–Cox Transformation to Determine the Standardised Precipitation Index (SPI, the Standardised Discharge Index (SDI and to Identify Drought Events: Case Study in Eastern Kujawy (Central Poland

Directory of Open Access Journals (Sweden)

Bartczak Arkadiusz

2014-10-01

Full Text Available The article presents the results of research into the transformation of series of hydro-meteorological data for determining dry periods with the Standardised Precipitation Index (SPI and the Standardised Discharge Index (SDI. Time series from eight precipitation stations and five series of river discharge data in Eastern Kujawy (central Poland were analysed for 1951–2010. The frequency distribution of the series for their convergence with the normal distribution was tested with the Shapiro–Wilk test and homogeneity with the Bartlett's test. The transformation of the series was done with the Box–Cox technique, which made it possible to homogenise the series in terms of variance. In Poland, the technique has never been used to determine the SPI. After the transformation the distributions of virtually all series complied with the normal distribution and were homogeneous. Moreover, a statistically significant correlation between the δ transformation parameter and the skewness of the series of monthly precipitation was observed. It was similar for the series of mean monthly discharges in the winter half-year and the hydrological year. The analysis indicates an alternate occurrence of dry and wet periods both in case of precipitation and run-offs. Drought periods coincided with low flow periods. Thus, the fluctuations tend to affect the development of agriculture more than long-term ones.

Employees' Motivation for SPI: Case Study in a Small Finnish Software Company

Science.gov (United States)

Valtanen, Anu; Sihvonen, Hanna-Miina

In small software companies the resources available for SPI are often limited. With limited resources, the motivation of the employees becomes one of the key factors for SPI. In this article, the motivational factors affecting a small company's SPI efforts are discussed. In the research, we carried out interviews and a survey in a small Finnish software company considering the motivation towards SPI. The results are presented here and compared with earlier motivation research. There were differences revealed while comparing the motivating factors of smaller companies to those of larger ones. In large companies the focus seems to be on the business related motivators and in small ones the motivators related to comfortability of work are emphasized. Motivation survey and the interviews proved to be useful tools in planning the future SPI strategy. A lot of valuable information was discovered for planning and implementing the next steps of SPI.

The Birth, Death, and Resurrection of an SPI Project

Science.gov (United States)

Carlsson, Sven; Schönström, Mikael

Commentators on contemporary themes of strategic management and firm competitiveness stress that a firm's competitive advantage flows from its unique knowledge and how it manages knowledge, and for many firms their ability to create, share, exchange, and use knowledge have a major impact on their competitiveness (Nonaka & Teece 2001). In software development, knowledge management (KM) plays an increasingly important role. It has been argued that the KM-field is an important source for creating new perspectives on the software development process (Iivari 2000). Several Software Process Improvement (SPI) approaches stress the importance of managing knowledge and experiences as a way for improving software processes (Ahem et al. 2001). Another SPI-trend is the use of ideas from process management like in the Capability Maturity Model (CMM). Unfortunately, little research on the effects of the use of process management ideas in SPI exists. Given the influx of process management ideas to SPI, the impact of these ideas should be addressed.

The Spy VI child : A newly discovered Neandertal infant

NARCIS (Netherlands)

Crevecoeur, Isabelle; Bayle, Priscilla; Rougier, Helene; Maureille, Bruno; Higham, Thomas; van der Plicht, Johannes; De Clerck, Nora; Semal, Patrick

2010-01-01

Spy cave (Jemeppe-sur-Sambre Belgium) is reputed for the two adult Neandertal individuals discovered in situ in 1886 Recent reassessment of the Spy collections has allowed direct radiocarbon dating of these individuals The sorting of all of the faunal collections has also led to the discovery of the

SPI Conformance Gel Applications in Geothermal Zonal Isolation

Energy Technology Data Exchange (ETDEWEB)

Burns, Lyle [Clean Tech Innovations, Bartlesville, OK (United States)

2017-08-08

Zonal isolation in geothermal injection and producing wells is important while drilling the wells when highly fractured geothermal zones are encountered and there is a need to keep the fluids from interfering with the drilling operation. Department of Energy’s (DOE) Energy Efficiency and Renewable Energy (EERE) objectives are to advance technologies to make it more cost effective to develop, produce, and monitor geothermal reservoirs and produce geothermal energy. Thus, zonal isolation is critical to well cost, reservoir evaluation and operations. Traditional cementing off of the lost circulation or thief zones during drilling is often done to stem the drilling mud losses. This is an expensive and generally unsuccessful technique losing the potential of the remaining fracture system. Selective placement of strong SPI gels into only the offending fractures can maintain and even improve operational efficiency and resource life. The SPI gel system is a unique silicate based gel system that offers a promising solution to thief zones and conformance problems with water and CO2 floods and potentially geothermal operations. This gel system remains a low viscosity fluid until an initiator (either internal such as an additive or external such as CO2) triggers gelation. This is a clear improvement over current mechanical methods of using packers, plugs, liners and cementing technologies that often severely damage the highly fractured area that is isolated. In the SPI gels, the initiator sets up the fluid into a water-like (not a precipitate) gel and when the isolated zone needs to be reopened, the SPI gel may be removed with an alkaline solution without formation damage occurring. In addition, the SPI gel in commercial quantities is expected to be less expensive than competing mechanical systems and has unique deep placement possibilities. This project seeks to improve upon the SPI gel integrity by modifying the various components to impart temperature stability for use in

INTEGRAL/SPI γ-ray line spectroscopy. Response and background characteristics

Science.gov (United States)

Diehl, Roland; Siegert, Thomas; Greiner, Jochen; Krause, Martin; Kretschmer, Karsten; Lang, Michael; Pleintinger, Moritz; Strong, Andrew W.; Weinberger, Christoph; Zhang, Xiaoling

2018-03-01

Context. The space based γ-ray observatory INTEGRAL of the European Space Agency (ESA) includes the spectrometer instrument "SPI". This is a coded mask telescope featuring a 19-element Germanium detector array for high-resolution γ-ray spectroscopy, encapsulated in a scintillation detector assembly that provides a veto for background from charged particles. In space, cosmic rays irradiate spacecraft and instruments, which, in spite of the vetoing detectors, results in a large instrumental background from activation of those materials, and leads to deterioration of the charge collection properties of the Ge detectors. Aim. We aim to determine the measurement characteristics of our detectors and their evolution with time, that is, their spectral response and instrumental background. These incur systematic variations in the SPI signal from celestial photons, hence their determination from a broad empirical database enables a reduction of underlying systematics in data analysis. For this, we explore compromises balancing temporal and spectral resolution within statistical limitations. Our goal is to enable modelling of background applicable to spectroscopic studies of the sky, accounting separately for changes of the spectral response and of instrumental background. Methods: We use 13.5 years of INTEGRAL/SPI data, which consist of spectra for each detector and for each pointing of the satellite. Spectral fits to each such spectrum, with independent but coherent treatment of continuum and line backgrounds, provides us with details about separated background components. From the strongest background lines, we first determine how the spectral response changes with time. Applying symmetry and long-term stability tests, we eliminate degeneracies and reduce statistical fluctuations of background parameters, with the aim of providing a self-consistent description of the spectral response for each individual detector. Accounting for this, we then determine how the

Assessment of meteorological drought using SPI in West Azarbaijan ...

African Journals Online (AJOL)

Michael Horsfall

2017-05-13

May 13, 2017 ... The basis of drought indices is often based on measuring the deviation of precipitation values from long-term mean, during a specific period of time. The standard precipitation index (SPI) can be used for indicating the associated temporal and spatial variations. The aim of this research is the assessment of ...

klanke, voëlgeluide en musiek in die digkuns van Lina Spies

African Journals Online (AJOL)

27 Jan 2006 ... 1999: 11-23), maak Spies self ook nie melding van musiek as tema nie; sy noem die. Afrikaanse digteres se “siening van die natuur, die religie, die politiek, medekunste- naars, verwantskap, digterskap” (Spies 1999: 22). Maar die digter verklaar in 'n onder- houd (Spies 2004b) dat sy drie groot passies het: ...

Improved Mobility Control for Carbon Dioxide (CO{sub 2}) Enhanced Oil Recovery Using Silica-Polymer-Initiator (SPI) Gels

Energy Technology Data Exchange (ETDEWEB)

Oglesby, Kenneth

2014-01-31

SPI gels are multi-component silicate based gels for improving (areal and vertical) conformance in oilfield enhanced recovery operations, including water-floods and carbon dioxide (CO{sub 2}) floods, as well as other applications. SPI mixtures are like-water when pumped, but form light up to very thick, paste-like gels in contact with CO{sub 2}. When formed they are 3 to 10 times stronger than any gelled polyacrylamide gel now available, however, they are not as strong as cement or epoxy, allowing them to be washed / jetted out of the wellbore without drilling. This DOE funded project allowed 8 SPI field treatments to be performed in 6 wells (5 injection wells and 1 production well) in 2 different fields with different operators, in 2 different basins (Gulf Coast and Permian) and in 2 different rock types (sandstone and dolomite). Field A was in a central Mississippi sandstone that injected CO{sub 2} as an immiscible process. Field B was in the west Texas San Andres dolomite formation with a mature water-alternating-gas miscible CO{sub 2} flood. Field A treatments are now over 1 year old while Field B treatments have only 4 months data available under variable WAG conditions. Both fields had other operational events and well work occurring before/ during / after the treatments making definitive evaluation difficult. Laboratory static beaker and dynamic sand pack tests were performed with Ottawa sand and both fields’ core material, brines and crude oils to improve SPI chemistry, optimize SPI formulations, ensure SPI mix compatibility with field rocks and fluids, optimize SPI treatment field treatment volumes and methods, and ensure that strong gels set in the reservoir. Field quality control procedures were designed and utilized. Pre-treatment well (surface) injectivities ranged from 0.39 to 7.9 MMCF/psi. The SPI treatment volumes ranged from 20.7 cubic meters (m{sup 3}, 5460 gallons/ 130 bbls) to 691 m{sup 3} (182,658 gallons/ 4349 bbls). Various size and types

Learning and Organizational Change in SPI Initiatives

Science.gov (United States)

Heikkilä, Marikka

Explaining how organizations chance has been a central and enduring quest of management scholars and many other disciplines. In order to be successful change requires not only a new process or technology but also the engagement and participation of the people involved. In this vein the change process results in new behavior and is routinized in practical daily business life of the company. Change management provides a framework for managing the human side of these changes. In this article we present a literature review on the change management in the context of Software Process Improvement. The traditional view of learning, as a “lessons learned” or post-mortem reporting activity is often apparent in SPI literature. However, learning can also be viewed as a continuous change process where specific learning cycle starts with creative conflict and ends up in formal norms and systems. Since this perspective has almost no visibility in SPI literature of past it could show a new direction to the future development of change management in SPI.

Development of SPIES (Space Intelligent Eyeing System) for smart vehicle tracing and tracking

Science.gov (United States)

Abdullah, Suzanah; Ariffin Osoman, Muhammad; Guan Liyong, Chua; Zulfadhli Mohd Noor, Mohd; Mohamed, Ikhwan

2016-06-01

SPIES or Space-based Intelligent Eyeing System is an intelligent technology which can be utilized for various applications such as gathering spatial information of features on Earth, tracking system for the movement of an object, tracing system to trace the history information, monitoring driving behavior, security and alarm system as an observer in real time and many more. SPIES as will be developed and supplied modularly will encourage the usage based on needs and affordability of users. SPIES are a complete system with camera, GSM, GPS/GNSS and G-Sensor modules with intelligent function and capabilities. Mainly the camera is used to capture pictures and video and sometimes with audio of an event. Its usage is not limited to normal use for nostalgic purpose but can be used as a reference for security and material of evidence when an undesirable event such as crime occurs. When integrated with space based technology of the Global Navigational Satellite System (GNSS), photos and videos can be recorded together with positioning information. A product of the integration of these technologies when integrated with Information, Communication and Technology (ICT) and Geographic Information System (GIS) will produce innovation in the form of information gathering methods in still picture or video with positioning information that can be conveyed in real time via the web to display location on the map hence creating an intelligent eyeing system based on space technology. The importance of providing global positioning information is a challenge but overcome by SPIES even in areas without GNSS signal reception for the purpose of continuous tracking and tracing capability

SpyRings Declassified: A Blueprint for Using Isopeptide-Mediated Cyclization to Enhance Enzyme Thermal Resilience.

Science.gov (United States)

Schoene, C; Bennett, S P; Howarth, M

2016-01-01

Enzymes often have marginal stability, with unfolding typically leading to irreversible denaturation. This sensitivity is a major barrier, both for de novo enzyme development and for expanding enzyme impact beyond the laboratory. Seeking an approach to enhance resilience to denaturation that could be applied to a range of different enzymes, we developed SpyRing cyclization. SpyRings contain genetically encoded SpyTag (13 amino acids) on the N-terminus and SpyCatcher (12kDa) on the C-terminus of the enzyme, so that the Spy partners spontaneously react together through an irreversible isopeptide bond. SpyRing cyclization gave major increases in thermal resilience, including on a model for enzyme evolution, β-lactamase, and an industrially important enzyme in agriculture and nutrition, phytase. We outline the SpyRing rationale, including comparison of SpyRing cyclization to other cyclization strategies. The cloning strategy is presented for the simple insertion of enzyme genes for recombinant expression. We discuss structure-based approaches to select suitable enzyme cyclization targets. Approaches to evaluate the cyclization reaction and its effect on enzyme resilience are described. We also highlight the use of differential scanning calorimetry to understand how SpyRing cyclization promotes enzyme refolding. Efficiently searching sequence space will continue to be important for enzyme improvement, but the SpyRing platform may be a valuable rational adjunct for conferring resilience. © 2016 Elsevier Inc. All rights reserved.

Salmonella Typhimurium induces SPI-1 and SPI-2 regulated and strain dependent downregulation of MHC II expression on porcine alveolar macrophages

Directory of Open Access Journals (Sweden)

Van Parys Alexander

2012-06-01

Full Text Available Abstract Foodborne salmonellosis is one of the most important bacterial zoonotic diseases worldwide. Salmonella Typhimurium is the serovar most frequently isolated from persistently infected slaughter pigs in Europe. Circumvention of the host’s immune system by Salmonella might contribute to persistent infection of pigs. In the present study, we found that Salmonella Typhimurium strain 112910a specifically downregulated MHC II, but not MHC I, expression on porcine alveolar macrophages in a Salmonella pathogenicity island (SPI-1 and SPI-2 dependent way. Salmonella induced downregulation of MHC II expression and intracellular proliferation of Salmonella in macrophages were significantly impaired after opsonization with Salmonella specific antibodies prior to inoculation. Furthermore, the capacity to downregulate MHC II expression on macrophages differed significantly among Salmonella strains, independently of strain specific differences in invasion capacity, Salmonella induced cytotoxicity and altered macrophage activation status. The fact that strain specific differences in MHC II downregulation did not correlate with the extent of in vitro SPI-1 or SPI-2 gene expression indicates that other factors are involved in MHC II downregulation as well. Since Salmonella strain dependent interference with the pig’s immune response through downregulation of MHC II expression might indicate that certain Salmonella strains are more likely to escape serological detection, our findings are of major interest for Salmonella monitoring programs primarily based on serology.

Magnifying the ordinary: genre mixture and humour in the TV series 'Chuck'

OpenAIRE

Gregori-Signes, Carmen

2012-01-01

Chuck is an American television series about a "normal" guy who works at the Buy More. His life changes dramatically when he becomes the most important person for the USA intelligence. This gives rise to a conflict between civilian and spy life. The analysis involves a multimodal account (cf. Lorenzo-Dus 2009) of the aesthetics (Cardwell 2005) of the second season that illustrates how the series establishes a parallelism between Chuck's life as a spy and that of his colleagues at the Buy More...

SPiDer: Smart Pill Dispenser

OpenAIRE

Moreno Vázquez, Jonatan

2013-01-01

Aquest projecte proposa el sistema SPiDer el qual te l'objectiu d'assistir a la gent gran en el procés de medicar-se evitant els problemes associats amb la baixa adherència existent actualment amb els calendaris farmacològics receptats pels doctors.

Assessment of the Standardized Precipitation Index (SPI) in Tegal City, Central Java, Indonesia

Science.gov (United States)

Pramudya, Y.; Onishi, T.

2018-03-01

One of the adverse impacts of climate change is drought, which occurs more frequently in Tegal city, Indonesia. The application of drought index analysis is useful for drought assessment to consider adaptation and mitigation method in order to deal with climate change. By figuring out the level and duration of the drought. In order to analyze drought in the specific area, Standardized Precipitation Index (SPI) is an index to quantify the rainfall deficit for multiple timescales. In 2015, Indonesia experienced severe drought, which has not been analyzed, yet. Thus, it is important to assess a quantitative evaluation of the drought condition. The study shows that from all deficit periods, the most severe drought in duration and peak took place in 2015, with each drought index as follows: 1 month deficit or SPI-1 (-3.11) in 1985 (-2.51) in 2015, 3 month deficit or SPI-3 (-2.291) in 1995 (-1.82) in 2015, 6 month deficit or SPI-6 (-2.40) in 1997 and (-1.84) in 2015, 9 month deficit or SPI-9 (-1.12) in 2015, 12 month deficit or SPI-12 (-1.19) in 2015. The result underlines the potential that SPI exhibits in drought identification and the use of the rainfall strongly linked to drought relief policy and measure implementation in Tegal city.

The γ-ray burst-detection system of SPI

International Nuclear Information System (INIS)

Lichti, G.G.; Georgii, R.; Kienlin, A. von; Schoenfelder, V.; Wunderer, C.; Jung, H.-J.; Hurley, K.

2000-01-01

The determination of precise locations of γ-ray bursts is a crucial task of γ-ray astronomy. Although γ-ray burst locations can be obtained nowadays from single experiments (BATSE, COMPTEL, BeppoSax) the location of bursts via triangulation using the interplanetary network is still important because not all bursts will be located precisely enough by these single instruments. In order to get location accuracies down to arcseconds via triangulation one needs long baselines. At the beginning of the next decade several spacecrafts which explore the outer planetary system (the Mars-Surveyor-2001 Orbiter and probably Ulysses) will carry γ-ray burst instruments. INTEGRAL as a near-earth spacecraft is the ideal counterpart for these satellites. The massive anticoincidence shield of the INTEGRAL-spectrometer SPI allows the measurement of γ-ray bursts with a high sensitivity. Estimations have shown that with SPI some hundred γ-ray bursts per year on the 5σ level can be measured. This is equivalent to the BATSE sensitivity. We describe the γ-ray burst-detection system of SPI, present its technical features and assess the scientific capabilities

Integration of a complex regulatory cascade involving the SirA/BarA and Csr global regulatory systems that controls expression of the Salmonella SPI-1 and SPI-2 virulence regulons through HilD.

Science.gov (United States)

Martínez, Luary C; Yakhnin, Helen; Camacho, Martha I; Georgellis, Dimitris; Babitzke, Paul; Puente, José L; Bustamante, Víctor H

2011-06-01

Salmonella pathogenicity islands 1 and 2 (SPI-1 and SPI-2) play key roles in the pathogenesis of Salmonella enterica. Previously, we showed that when Salmonella grows in Luria-Bertani medium, HilD, encoded in SPI-1, first induces the expression of hilA, located in SPI-1, and subsequently of the ssrAB operon, located in SPI-2. These genes code for HilA and the SsrA/B two-component system, the positive regulators of the SPI-1 and SPI-2 regulons respectively. In this study, we demonstrate that CsrA, a global regulatory RNA binding protein, post-transcriptionally regulates hilD expression by directly binding near the Shine-Dalgarno and translation initiation codon sequences of the hilD mRNA, preventing its translation and leading to its accelerated turnover. Negative regulation is counteracted by the global SirA/BarA two-component system, which directly activates the expression of CsrB and CsrC, two non-coding regulatory RNAs that sequester CsrA, thereby preventing it from binding to its target mRNAs. Our results illustrate the integration of global and specific regulators into a multifactorial regulatory cascade controlling the expression of virulence genes acquired by horizontal transfer events. © 2011 Blackwell Publishing Ltd.

Harnessing ISO/IEC 12207 to Examine the Extent of SPI Activity in an Organisation

Science.gov (United States)

Clarke, Paul; O'Connor, Rory

The quality of the software development process directly affects the quality of the software product. To be successful, software development organisations must respond to changes in technology and business circumstances, and therefore software process improvement (SPI) is required. SPI activity relates to any modification that is performed to the software process in order to improve an aspect of the process. Although multiple process assessments could be employed to examine SPI activity, they present an inefficient tool for such an examination. This paper presents an overview of a new survey-based resource that utilises the process reference model in ISO/IEC 12207 in order to expressly and directly determine the level of SPI activity in a software development organisation. This survey instrument can be used by practitioners, auditors and researchers who are interested in determining the extent of SPI activity in an organisation.

Binaries discovered by the SPY project V. GD 687 - a massive double degenerate binary progenitor that will merge within a Hubble time

OpenAIRE

Geier, S.; Heber, U.; Kupfer, T.; Napiwotzki, R.

2010-01-01

Aims. The ESO SN Ia Progenitor Survey (SPY) aims at finding merging double degenerate binaries as candidates for supernova type Ia (SN Ia) explosions. A white dwarf merger has also been suggested to explain the formation of rare types of stars like R CrB, extreme helium or He sdO stars. Here we present the hot subdwarf B binary GD 687, which will merge in less than a Hubble time. Methods. The orbital parameters of the close binary have been determined from time resolved spectroscopy. Since GD...

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

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.

INTEGRAL: In flight behavior of ISGRI and SPI

International Nuclear Information System (INIS)

Lebrun, F.; Roques, J.-P.; Sauvageon, A.; Terrier, R.; Laurent, P.; Limousin, O.; Lugiez, F.; Claret, A.

2005-01-01

The payload of INTEGRAL, the space gamma-ray observatory launched in October 2002, features two gamma-ray telescopes that take advantage of the semiconductor technologies. The spectrometer SPI, is equipped with 19 high-purity germanium detectors cooled at 85 K. We will report on the SPI in-flight background, performance, the detector evolution and the annealings performed every 6 months. The INTEGRAL Soft Gamma-Ray Imager (ISGRI) is the low-energy camera of the IBIS telescope. It is the first large camera equipped with CdTe detectors. We will present some system aspects, in particular the noisy pixel handling and will report on its in-flight background, performance and their evolution

Power potential and the energy in the Spiš region

Directory of Open Access Journals (Sweden)

Major Michal

2001-12-01

Full Text Available The contribution is a short review of the energy power information about three districts: Spišská Nová Ves, Gelnica and Levoèa. These districts are parts of the county of Košice and Prešov. The contribution contains a summary of basic illustration information about the sources of energy and about a consumption of electric and heat energy in the area. In the area of the region, there is no standard thermal power station and no nuclear power station. From own sources of the region, there is especially a water energy. The majority of heat and electric energy, which is necessary in the region, comes from outside sources. The sources of power are situated in Spišská Nová Ves and Gelnica districts. The Levoèa district has no own source of energy.In the Spišská Nová Ves district, there is one industrial establishment, which produces the heat and electric energy for the own use and also for the export. With the exception of this one establishment, in Spišská district, there are 3 little hydro - electric power plants with the total installed power capacity 138 kW. And there is also one energy unit for the output of electricity and heat with the installed power capacity 400 kW. In the area of Gelnica, there is one hydro – electric power plant, in the water reservoir named Ružín. It has installed the power capacity 2x30 MW. There are also 7 little hydro - electric power plants with the total installed power capacity 528 kW and one energy unit for the output of electricity and heat with installed power capacity 1100 kW.The future development of own energy sources in the region is oriented to the exploitation of natural energy sources, especially to the water energy and energy from biological gas. There is project of using the biological gas for the power purpose from one dump in the Spišská Nová Ves district. It will be realised in the time period of 10 years. In all listed above districts measurements are realized, which will show if

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

Characterization of the in-flight degradation of the INTEGRAL/SPI detectors

International Nuclear Information System (INIS)

Lonjou, V.; Roques, J.P.; Ballmoos, P. von; Jean, P.; Knodlseder, J.; Skinner, G.; Thevenin, A.; Weidenspointner, G.

2005-01-01

SPI is a high spectral resolution gamma ray telescope which was launched on 2002 October 17 on-board INTEGRAL (INTErnational Gamma Ray Astrophysics Laboratory). The SPI camera consists of 19 high-purity germanium detectors that cover an energy range of 20 keV-8 MeV with an energy resolution of 2-8 keV FWHM. We describe the methods used for the determination of the effects of radiation damage on the SPI detectors. Degradation rate and recovery by annealing are quantified. Using instrumental background lines due to radioisotopes from natural decay chains and from cosmic ray interactions, we found that the variations of detectors efficiency are low. Finally, the impact of the detector degradation on the energy calibration has been investigated

The SPI-1-like Type III secretion system: more roles than you think.

Science.gov (United States)

Egan, Frank; Barret, Matthieu; O'Gara, Fergal

2014-01-01

The type III secretion system (T3SS) is a protein delivery system which is involved in a wide spectrum of interactions, from mutualism to pathogenesis, between Gram negative bacteria and various eukaryotes, including plants, fungi, protozoa and mammals. Various phylogenetic families of the T3SS have been described, including the Salmonella Pathogenicity Island 1 family (SPI-1). The SPI-1 T3SS was initially associated with the virulence of enteric pathogens, but is actually found in a diverse array of bacterial species, where it can play roles in processes as different as symbiotic interactions with insects and colonization of plants. We review the multiple roles of the SPI-1 T3SS and discuss both how these discoveries are changing our perception of the SPI-1 family and what impacts this has on our understanding of the specialization of the T3SS in general.

The SPI-1-like Type III secretion system: more roles than you think

Science.gov (United States)

Egan, Frank; Barret, Matthieu; O’Gara, Fergal

2014-01-01

The type III secretion system (T3SS) is a protein delivery system which is involved in a wide spectrum of interactions, from mutualism to pathogenesis, between Gram negative bacteria and various eukaryotes, including plants, fungi, protozoa and mammals. Various phylogenetic families of the T3SS have been described, including the Salmonella Pathogenicity Island 1 family (SPI-1). The SPI-1 T3SS was initially associated with the virulence of enteric pathogens, but is actually found in a diverse array of bacterial species, where it can play roles in processes as different as symbiotic interactions with insects and colonization of plants. We review the multiple roles of the SPI-1 T3SS and discuss both how these discoveries are changing our perception of the SPI-1 family and what impacts this has on our understanding of the specialization of the T3SS in general. PMID:24575107

Spatial and temporal analysis of drought in greece using the Standardized Precipitation Index (SPI)

Science.gov (United States)

Livada, I.; Assimakopoulos, V. D.

2007-07-01

In the present study the Standardised Precipitation Index (SPI) is used to detect drought events in spatial and temporal basis. Using monthly precipitation data from 23 stations well spread over Greece and for a period of 51 years, a classification of drought is performed, based on its intensity and duration. Results indicate that, mild and moderate droughts reduce from north to south and from west to east on the 3- and 6-months time scale, while for the class of severe drought, the frequencies in the southern part of Greece are higher than in the other parts of the country. Furthermore the frequency of occurrence of severe and extreme drought conditions is very low over the whole Greek territory on the 12-month running time scale. Finally SPI was compared to the “de Martonne aridity index (I)” and a satisfactory correlation between them was found.

Identification of cognate host targets and specific ubiquitylation sites on the Salmonella SPI-1 effector SopB/SigD

DEFF Research Database (Denmark)

Rogers, Lindsay D; Kristensen, Anders R; Boyle, Erin C

2008-01-01

Salmonella enterica is a bacterial pathogen responsible for enteritis and typhoid fever. Virulence is linked to two Salmonella pathogenicity islands (SPI-1 and SPI-2) on the bacterial chromosome, each of which encodes a type III secretion system. While both the SPI-1 and SPI-2 systems secrete...

TargetSpy: a supervised machine learning approach for microRNA target prediction.

Science.gov (United States)

Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

2010-05-28

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila

TargetSpy: a supervised machine learning approach for microRNA target prediction

Directory of Open Access Journals (Sweden)

Langenberger David

2010-05-01

Full Text Available Abstract Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I no seed match requirement, II seed match requirement, and III conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on

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...

PREPARATION AND CHARACTERIZATION OF SOY PROTEIN ISOLATE(SPI)/MONTMORILLONITE(MMT) BIONANOCOMPOSITES

Institute of Scientific and Technical Information of China (English)

傅强

2009-01-01

The bionanocomposites of soy protein isolate(SPI)/montmorillonite(MMT) have been prepared successfully via simple melt mixing,in which MMT was used as nanofiller and glycerol was used as plasticizer.Their structures and properties were characterized with X-ray diffraction(XRD),differential scanning calorimetry(DSC),scanning electron microscopy(SEM),thermogravimetric analysis and tensile testing.XRD、TEM and SEM results indicated that the MMT layers could be easily intercalated by the SPI matrix even by si...

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 UNCCD Science-Policy Interface (SPI) - Exploring the sustainable land management nexus among the Rio Conventions

Science.gov (United States)

Safriel, Uriel; Akhtar-Schuster, Mariam; Abraham, Elena Maria; Cowie, Annette; Daradur, Mihail; de Vente, Joris; Dema Dorji, Karma; Kust, German; Metternicht, Graciela; Orr, Barron; Pietragalla, Vanina

2015-04-01

At its 11th meeting in Windhoek/Namibia, in September 2013, the United Nations Convention to Combat Desertification (UNCCD) Conference of the Parties (COP) decided to establish a Science-Policy Interface (SPI)* (decision 23/COP.11). The goal of the SPI is to facilitate a two-way dialogue between scientists and policy makers in order to ensure the delivery of policy-relevant information, knowledge and advice on desertification/land degradation and drought (DLDD). The SPI established several initial objectives, including working with the scientific community to bring to the UNCCD and the other Rio conventions (climate change and biodiversity) the scientific evidence for the contribution of sustainable land use and management to climate change adaptation/mitigation and to safeguarding biodiversity and ecosystem services. *For more on the SPI see: http://www.unccd.int/en/programmes/Science/International-Scientific-Advice/Pages/SPI.aspx?HighlightID=282

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...

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

Spy and Counterspy as a “Cultural Hero” in the Soviet Cinema of the Cold War

Directory of Open Access Journals (Sweden)

Viktoria Sukovataya

2017-07-01

Full Text Available This article aim to analyze the evolution of the Soviet spy cinema of the Cold War in the context of the cultural history and the social changes in the USA and the Soviet Union, and the relations with the political opponents. The public reception of the Soviet spy and spying was evolved in the Soviet Union and it was reflected in the cinema plots and characters transformations.

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...

The sample of INTEGRAL SPI-ACS gamma-ray bursts

International Nuclear Information System (INIS)

Rau, A.; Kienlin, A. von; Licht, G.G.; Hurley, K.

2005-01-01

The anti-coincidence system of the spectrometer on board INTEGRAL is operated as a nearly omni directional gamma-ray burst detector above ∼ 75 KeV. During the elapsed mission time 324 burst candidates were detected. As part of the 3rd Interplanetary Network of gamma-ray detectors the cosmic origin of 115 burst was confirmed. Here we present a preliminary analysis of the SPI-ACS gamma-ray burst sample. In particular we discuss the origin of a significant population of short events (duration < 0.2 s) and a possible method for a flux calibration of the data

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.

SpyRing interrogation: analyzing how enzyme resilience can be achieved with phytase and distinct cyclization chemistries

Science.gov (United States)

Schoene, Christopher; Bennett, S. Paul; Howarth, Mark

2016-01-01

Enzymes catalyze reactions with exceptional selectivity and rate acceleration but are often limited by instability. Towards a generic route to thermo-resilience, we established the SpyRing approach, cyclizing enzymes by sandwiching between SpyTag and SpyCatcher (peptide and protein partners which lock together via a spontaneous isopeptide bond). Here we first investigated the basis for this resilience, comparing alternative reactive peptide/protein pairs we engineered from Gram-positive bacteria. Both SnoopRing and PilinRing cyclization gave dramatic enzyme resilience, but SpyRing cyclization was the best. Differential scanning calorimetry for each ring showed that cyclization did not inhibit unfolding of the inserted β-lactamase. Cyclization conferred resilience even at 100 °C, where the cyclizing domains themselves were unfolded. Phytases hydrolyze phytic acid and improve dietary absorption of phosphate and essential metal ions, important for agriculture and with potential against human malnutrition. SpyRing phytase (PhyC) resisted aggregation and retained catalytic activity even following heating at 100 °C. In addition, SpyRing cyclization made it possible to purify phytase simply by heating the cell lysate, to drive aggregation of non-cyclized proteins. Cyclization via domains forming spontaneous isopeptide bonds is a general strategy to generate resilient enzymes and may extend the range of conditions for isolation and application of enzymes. PMID:26861173

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 ...

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

Science.gov (United States)

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

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.

A Half-Day Workshop on ``Smarter Investment by Aligning SPI Initiatives, Capabilities and Stakeholder Values''

Science.gov (United States)

Selioukova, Yana; Frühwirth, Christian

Software companies who want to improve software process capabilities (SPCs)a systematic method to make informed investment decisions on software process improvement (SPI) initiatives. Such decisions should aim at creating maximum stakeholder values. To address this problem, we present a method with tool support that may help companies align stakeholder values with SPCs and SPI initiatives. The proposed method has been developed based on the well-established “Quality Function Deployment” (QFD) approach. The experience with the proposed method suggests that it particularly helps to reduce the risk of misalignment by identifying those SPI initiatives that are most beneficial to stakeholders. The tool support provided with the proposed method also generated positive experiences in increasing the usability of the method and helped companies in the elicitation and prioritization of stakeholder values. Therefore, we propose a workshop for the method work out named “Smarter Investment by Aligning SPI Initiatives, Capabilities and Stakeholder Values” in hypothetical case company.

Correction: The role of coupled positive feedback in the expression of the SPI1 type three secretion system in Salmonella.

Directory of Open Access Journals (Sweden)

Supreet Saini

2010-08-01

Full Text Available Salmonella enterica serovar Typhimurium is a common food-borne pathogen that induces inflammatory diarrhea and invades intestinal epithelial cells using a type three secretion system (T3SS encoded within Salmonella pathogenicity island 1 (SPI1. The genes encoding the SPI1 T3SS are tightly regulated by a network of interacting transcriptional regulators involving three coupled positive feedback loops. While the core architecture of the SPI1 gene circuit has been determined, the relative roles of these interacting regulators and associated feedback loops are still unknown. To determine the function of this circuit, we measured gene expression dynamics at both population and single-cell resolution in a number of SPI1 regulatory mutants. Using these data, we constructed a mathematical model of the SPI1 gene circuit. Analysis of the model predicted that the circuit serves two functions. The first is to place a threshold on SPI1 activation, ensuring that the genes encoding the T3SS are expressed only in response to the appropriate combination of environmental and cellular cues. The second is to amplify SPI1 gene expression. To experimentally test these predictions, we rewired the SPI1 genetic circuit by changing its regulatory architecture. This enabled us to directly test our predictions regarding the function of the circuit by varying the strength and dynamics of the activating signal. Collectively, our experimental and computational results enable us to deconstruct this complex circuit and determine the role of its individual components in regulating SPI1 gene expression dynamics.

Mysteries and Conspiracies: Detective Stories, Spy Novels And The Making Of Modern Societies

DEFF Research Database (Denmark)

Ossandón, José

2016-01-01

Book review of: Mysteries and Conspiracies: Detective Stories, Spy Novels and the Making of Modern Societies. By Luc Boltanski (trans. C Porter). Cambridge: Polity Press, 2014. xviii+340 pp. £18.99/€23.80. ISBN: 9780745664057.......Book review of: Mysteries and Conspiracies: Detective Stories, Spy Novels and the Making of Modern Societies. By Luc Boltanski (trans. C Porter). Cambridge: Polity Press, 2014. xviii+340 pp. £18.99/€23.80. ISBN: 9780745664057....

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

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

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...

Treatment Using the SpyGlass Digital System in a Patient with Hepatolithiasis after a Whipple Procedure.

Science.gov (United States)

Harima, Hirofumi; Hamabe, Kouichi; Hisano, Fusako; Matsuzaki, Yuko; Itoh, Tadahiko; Sanuki, Kazutoshi; Sakaida, Isao

2018-05-23

An 89-year-old man was referred to our hospital for treatment of hepatolithiasis causing recurrent cholangitis. He had undergone a prior Whipple procedure. Computed tomography demonstrated left-sided hepatolithiasis. First, we conducted peroral direct cholangioscopy (PDCS) using an ultraslim endoscope. Although PDCS was successfully conducted, it was unsuccessful in removing all the stones. The stones located in the B2 segment were difficult to remove because the endoscope could not be inserted deeply into this segment due to the small size of the intrahepatic bile duct. Next, we substituted the endoscope with an upper gastrointestinal endoscope. After positioning the endoscope, the SpyGlass digital system (SPY-DS) was successfully inserted deep into the B2 segment. Upon visualizing the residual stones, we conducted SPY-DS-guided electrohydraulic lithotripsy. The stones were disintegrated and completely removed. In cases of PDCS failure, a treatment strategy using the SPY-DS can be considered for patients with hepatolithiasis after a Whipple procedure.

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.

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.

Reliability and validity of the Korean standard pattern identification for stroke (K-SPI-Stroke questionnaire

Directory of Open Access Journals (Sweden)

Kang Byoung-Kab

2012-04-01

Full Text Available Abstract Background The present study was conducted to examine the reliability and validity of the ‘Korean Standard Pattern Identification for Stroke (K-SPI-Stroke’, which was developed and evaluated within the context of traditional Korean medicine (TKM. Methods Between September 2006 and December 2010, 2,905 patients from 11 Korean medical hospitals were asked to complete the K-SPI-Stroke questionnaire as a part of project ' Fundamental study for the standardization and objectification of pattern identification in traditional Korean medicine for stroke (SOPI-Stroke. Each patient was independently diagnosed by two TKM physicians from the same site according to one of four patterns, as suggested by the Korea Institute of Oriental Medicine: 1 a Qi deficiency pattern, 2 a Dampness-phlegm pattern, 3 a Yin deficiency pattern, or 4 a Fire-heat pattern. We estimated the internal consistency using Cronbach’s α coefficient, the discriminant validity using the means score of patterns, and the predictive validity using the classification accuracy of the K-SPI-Stroke questionnaire. Results The K-SPI-Stroke questionnaire had satisfactory internal consistency (α = 0.700 and validity, with significant differences in the mean of scores among the four patterns. The overall classification accuracy of this questionnaire was 65.2 %. Conclusion These results suggest that the K-SPI-Stroke questionnaire is a reliable and valid instrument for estimating the severity of the four patterns.

InSpiRe - Intelligent Spine Rehabilitation

DEFF Research Database (Denmark)

Bøg, Kasper Hafstrøm; Helms, Niels Henrik; Kjær, Per

InSpiRe er et projekt, der har haft omdrejningspunkt i etableringen af et nyt netværk indenfor intelligent genoptræning med særligt fokus på rygsmerter. Projektet er gennemført i perioden 1/3 2011 2011-1/3 2012, med støtte fra Syddansk Vækstforum, og er blevet drevet af projektparterne Knowledge ...... Lab, Syddansk Universitet (SDU), Institut for Idræt og Biomekanik (IoB), SDU, samt University College Lillebælt....

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.

Exploring Managerial Commitment towards SPI in Small and Very Small Enterprises

Science.gov (United States)

O'Connor, Rory V.; Basri, Shuib; Coleman, Gerry

This paper compares and contrasts the results of two similar studies into the software process practices in Irish Small and Very Small Enterprises. The first study contains rich findings in relation to the role and influence of managerial experience and style, with particular respect to the company founder and software development managers in small to medium seized enterprises (SMEs), whilst the second study contains extensive findings in relation to people and management involvement / commitment and SPI goal planning in very small enterprises (VSEs). By combining these results of these two studies of Irish SMEs/VSEs we can develop a rich picture of managerial commitment towards SPI and in particular explore the similarities between Small and Very Small Enterprises.

Harnessing ISO/IEC 12207 to examine the extent of SPI activity in an organisation

OpenAIRE

Clarke, Paul; O'Connor, Rory

2010-01-01

peer-reviewed The quality of the software development process directly affects the quality of the software product. To be successful, software development organisations must respond to changes in technology and business circumstances, and therefore software process improvement (SPI) is required. SPI activity relates to any modification that is performed to the software process in order to improve an aspect of the process. Although multiple process assessments could be employed to examine S...

Environmental sensing by mature B cells is controlled by the transcription factors PU.1 and SpiB.

Science.gov (United States)

Willis, Simon N; Tellier, Julie; Liao, Yang; Trezise, Stephanie; Light, Amanda; O'Donnell, Kristy; Garrett-Sinha, Lee Ann; Shi, Wei; Tarlinton, David M; Nutt, Stephen L

2017-11-10

Humoral immunity requires B cells to respond to multiple stimuli, including antigen, membrane and soluble ligands, and microbial products. Ets family transcription factors regulate many aspects of haematopoiesis, although their functions in humoral immunity are difficult to decipher as a result of redundancy between the family members. Here we show that mice lacking both PU.1 and SpiB in mature B cells do not generate germinal centers and high-affinity antibody after protein immunization. PU.1 and SpiB double-deficient B cells have a survival defect after engagement of CD40 or Toll-like receptors (TLR), despite paradoxically enhanced plasma cell differentiation. PU.1 and SpiB regulate the expression of many components of the B cell receptor signaling pathway and the receptors for CD40L, BAFF and TLR ligands. Thus, PU.1 and SpiB enable B cells to appropriately respond to environmental cues.

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

Modified intracellular-associated phenotypes in a recombinant Salmonella Typhi expressing S. Typhimurium SPI-3 sequences.

Directory of Open Access Journals (Sweden)

Patricio Retamal

Full Text Available A bioinformatics comparison of Salmonella Pathogenicity Island 3 sequences from S. Typhi and S. Typhimurium serovars showed that ten genes are highly conserved. However three of them are pseudogenes in S. Typhi. Our aim was to understand what functions are lost in S. Typhi due to pseudogenes by constructing a S. Typhi genetic hybrid carrying the SPI-3 region of S. Typhimurium instead of its own SPI-3. We observed that under stressful conditions the hybrid strain showed a clear impairment in resistance to hydrogen peroxide and decreased survival within U937 culture monocytes. We hypothesized that the marT-fidL operon, encoded in SPI-3, was responsible for the new phenotypes because marT is a pseudogen in S. Typhi and has a demonstrated role as a transcriptional regulator in S. Typhimurium. Therefore we cloned and transferred the S. Typhimurium marT-fidL operon into S. Typhi and confirmed that invasion of monocytes was dramatically decreased. Finally, our findings suggest that the genomic and functional differences between SPI-3 sequences have implications in the host specificity of Typhi and Typhimurium serovars.

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.

Crystal structure of Spy0129, a Streptococcus pyogenes class B sortase involved in pilus assembly.

Directory of Open Access Journals (Sweden)

Hae Joo Kang

2011-01-01

Full Text Available Sortase enzymes are cysteine transpeptidases that mediate the covalent attachment of substrate proteins to the cell walls of gram-positive bacteria, and thereby play a crucial role in virulence, infection and colonisation by pathogens. Many cell-surface proteins are anchored by the housekeeping sortase SrtA but other more specialised sortases exist that attach sub-sets of proteins or function in pilus assembly. The sortase Spy0129, or SrtC1, from the M1 SF370 strain of Streptococcus pyogenes is responsible for generating the covalent linkages between the pilin subunits in the pili of this organism. The crystal structure of Spy0129 has been determined at 2.3 Å resolution (R = 20.4%, Rfree  = 26.0%. The structure shows that Spy0129 is a class B sortase, in contrast to other characterised pilin polymerases, which belong to class C. Spy0129 lacks a flap believed to function in substrate recognition in class C enzymes and instead has an elaborated β6/β7 loop. The two independent Spy0129 molecules in the crystal show differences in the positions and orientations of the catalytic Cys and His residues, Cys221 and His126, correlated with movements of the β7/β8 and β4/β5 loops that respectively follow these residues. Bound zinc ions stabilise these alternative conformations in the crystal. This conformational variability is likely to be important for function although there is no evidence that zinc is involved in vivo.

Crystal structure of Spy0129, a Streptococcus pyogenes class B sortase involved in pilus assembly.

Science.gov (United States)

Kang, Hae Joo; Coulibaly, Fasséli; Proft, Thomas; Baker, Edward N

2011-01-11

Sortase enzymes are cysteine transpeptidases that mediate the covalent attachment of substrate proteins to the cell walls of gram-positive bacteria, and thereby play a crucial role in virulence, infection and colonisation by pathogens. Many cell-surface proteins are anchored by the housekeeping sortase SrtA but other more specialised sortases exist that attach sub-sets of proteins or function in pilus assembly. The sortase Spy0129, or SrtC1, from the M1 SF370 strain of Streptococcus pyogenes is responsible for generating the covalent linkages between the pilin subunits in the pili of this organism. The crystal structure of Spy0129 has been determined at 2.3 Å resolution (R = 20.4%, Rfree  = 26.0%). The structure shows that Spy0129 is a class B sortase, in contrast to other characterised pilin polymerases, which belong to class C. Spy0129 lacks a flap believed to function in substrate recognition in class C enzymes and instead has an elaborated β6/β7 loop. The two independent Spy0129 molecules in the crystal show differences in the positions and orientations of the catalytic Cys and His residues, Cys221 and His126, correlated with movements of the β7/β8 and β4/β5 loops that respectively follow these residues. Bound zinc ions stabilise these alternative conformations in the crystal. This conformational variability is likely to be important for function although there is no evidence that zinc is involved in vivo.

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.

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)""…

WebSpy: An Architecture for Monitoring Web Server Availability in a Multi-Platform Environment

Directory of Open Access Journals (Sweden)

Madhan Mohan Thirukonda

2002-01-01

Full Text Available For an electronic business (e-business, customer satisfaction can be the difference between long-term success and short-term failure. Customer satisfaction is highly impacted by Web server availability, as customers expect a Web site to be available twenty-four hours a day and seven days a week. Unfortunately, unscheduled Web server downtime is often beyond the control of the organization. What is needed is an effective means of identifying and recovering from Web server downtime in order to minimize the negative impact on the customer. An automated architecture, called WebSpy, has been developed to notify administration and to take immediate action when Web server downtime is detected. This paper describes the WebSpy architecture and differentiates it from other popular Web monitoring tools. The results of a case study are presented as a means of demonstrating WebSpy's effectiveness in monitoring Web server availability.

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.

Discrete wavelet transform-based investigation into the variability of standardized precipitation index in Northwest China during 1960-2014

Science.gov (United States)

Yang, Peng; Xia, Jun; Zhan, Chesheng; Zhang, Yongyong; Hu, Sheng

2018-04-01

In this study, the temporal variations of the standard precipitation index (SPI) were analyzed at different scales in Northwest China (NWC). Discrete wavelet transform (DWT) was used in conjunction with the Mann-Kendall (MK) test in this study. This study also investigated the relationships between original precipitation and different periodic components of SPI series with datasets spanning 55 years (1960-2014). The results showed that with the exception of the annual and summer SPI in the Inner Mongolia Inland Rivers Basin (IMIRB), spring SPI in the Qinghai Lake Rivers Basin (QLRB), and spring SPI in the Central Asia Rivers Basin (CARB), it had an increasing trend in other regions for other time series. In the spring, summer, and autumn series, though the MK trends test in most areas was at the insignificant level, they showed an increasing trend in precipitation. Meanwhile, the SPI series in most subbasins of NWC displayed a turning point in 1980-1990, with the significant increasing levels after 2000. Additionally, there was a significant difference between the trend of the original SPI series and the largest approximations. The annual and seasonal SPI series were composed of the short periodicities, which were less than a decade. The MK value would increase by adding the multiple D components (and approximations), and the MK value of the combined series was in harmony with that of the original series. Additionally, the major trend of the annual SPI in NWC was based on the four kinds of climate indices (e.g., Atlantic Oscillation [AO], North Atlantic Oscillation [NAO], Pacific Decadal Oscillation [PDO], and El Nino-Southern Oscillation index [ENSO/NINO]), especially the ENSO.

Towards the identification of the influence of SPI on the successful evolution of software SMEs

OpenAIRE

Clarke, Paul; O'Connor, Rory

2010-01-01

peer-reviewed Software development requires multi-stage processes in order to organise the software development effort. Each software development project should implement a development process that is appropriate to the project setting. Since business needs and technologies are subject to change, software process improvement (SPI) actions are required so as to harmonise the process with the emerging business and technology needs. SPI frameworks such as the Capability Maturity Model Integra...

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.

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....

INTEGRAL SPI Limits on Electron-Positron Annihilation Radiation from the Galactic Plane

DEFF Research Database (Denmark)

Teegarden, B. J.; Watanabe, K.; Jean, P.

2005-01-01

‐Ray Astrophysics Laboratory) mission, launched in 2002 October, is the detailed study of this radiation. The Spectrometer on INTEGRAL (SPI) is a high‐resolution, coded‐aperture gamma‐ray telescope with an unprecedented combination of sensitivity, angular resolution, and energy resolution. We report results from...... the first 10 months of observation. During this period a significant fraction of the observing time was spent in or near the Galactic plane. No positive annihilation flux was detected outside of the central region () of our Galaxy. In this paper we describe the observations and data analysis methods...

Application of the SPI (Saliva Precipitation Index) to the evaluation of red wine astringency.

Science.gov (United States)

Rinaldi, Alessandra; Gambuti, Angelita; Moio, Luigi

2012-12-15

The aim of this work was to evaluate the astringency of red wines by means of a SDS-PAGE based-method. The optimization of the in vitro assay, named SPI (Saliva Precipitation Index) that measured the reactivity of salivary proteins towards wine polyphenols, has been performed. Improvements included the choice of saliva:wine ratio, saliva typology (resting or stimulated saliva), and temperature of binding. The LOD (0.05 g/L of condensed tannin) and LOQ (0.1g/L of condensed tannin) for the binding reaction between salivary proteins and tannins added in white wine were also determined. Fifty-seven red wines were analysed by the optimised SPI, the Folin-Ciocalteu Index, the gelatine index, the content of total tannins and the sensory quantitative evaluation of astringency. A significant correlation between the SPI and the astringency of red wines was found (R(2)=0.969), thus indicating that this assay may be useful as estimator of astringency. Copyright © 2012 Elsevier Ltd. All rights reserved.

Breaking The Millisecond Barrier On SpiNNaker: Implementing Asynchronous Event-Based Plastic Models With Microsecond Resolution

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-06-01

Full Text Available Spike-based neuromorphic sensors such as retinas and cochleas, change the way in which the world is sampled. Instead of producing data sampled at a constant rate, these sensors output spikes that are asynchronous and event driven. The event-based nature of neuromorphic sensors implies a complete paradigm shift in current perception algorithms towards those that emphasize the importance of precise timing. The spikes produced by these sensors usually have a time resolution in the order of microseconds. This high temporal resolution is a crucial factor in learning tasks. It is also widely used in the field of biological neural networks. Sound localization for instance relies on detecting time lags between the two ears which, in the barn owl, reaches a temporal resolution of 5 microseconds. Current available neuromorphic computation platforms such as SpiNNaker often limit their users to a time resolution in the order of milliseconds that is not compatible with the asynchronous outputs of neuromorphic sensors. To overcome these limitations and allow for the exploration of new types of neuromorphic computing architectures, we introduce a novel software framework on the SpiNNaker platform. This framework allows for simulations of spiking networks and plasticity mechanisms using a completely asynchronous and event-based scheme running with a microsecond time resolution. Results on two example networks using this new implementation are presented.

Assessment Tools as Drivers for SPI: Short-term Benefits and Long-term Challenges

DEFF Research Database (Denmark)

Mûller, Sune Dueholm; Nørbjerg, Jacob; Cho, Hiu Ngan

2007-01-01

Full scale software process maturity assessments are costly, can have large organizational impact, and are carried out at long (12-24 months) intervals. Consequently, there is a need for techniques and tools to monitor and help manage an SPI project through inexpensive, ongoing progress assessments....... In this paper we present findings from two cases of using such a tool. We have found that the tool does provide useful snapshots of the status of SPI projects, but that long-term use of the tool introduces costs and challenges related to modifying and tailoring the tool to both the organizational context...

The Spies and the Gran Turco (XVI Century

Directory of Open Access Journals (Sweden)

Gennaro Varriale

2016-04-01

Full Text Available This present essay focuses on the information that Hispanic spies collected in the Levant during the sixteenth-century. Based on archival and literary sources, the study is built around the typical structure of a lyric opera to show the worth of the secret correspondence such as archetype of European perception about the Ottoman Empire and, more generally, about all the Islam.

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 novel approach for modeling the cluster detector and the SPI spectrometer

International Nuclear Information System (INIS)

Kshetri, Ritesh

2013-01-01

Using available experimental data on cluster detector, predictions for the peak-to-total ratio have been given for energy region having no direct experimental information about them. The predictions for the fold distribution are found to be in agreement with the experimental data. The formulation here does not include ad-hoc fits, but expressions that are justifiable by probability flow arguments. Instead of using an empirical method or simulation, a novel approach for calculating the peak-to-total ratio of the cluster detector and the SPI spectrometer for high gamma energies has been presented here. This could provide guidance in designing new composite detectors and in performing experimental studies with the SPI spectrometer for high energy gamma-rays

Observation of the decay $B_c^+ \\to \\psi(2S)\\pi^+$

CERN Document Server

INSPIRE-00258707; Abellan Beteta, C; Adeva, B; Adinolfi, M; Adrover, C; Affolder, A; Ajaltouni, Z; Albrecht, J; Alessio, F; Alexander, M; Ali, S; Alkhazov, G; Alvarez Cartelle, P; Alves Jr, A A; Amato, S; Amerio, S; Amhis, Y; Anderlini, L; Anderson, J; Andreassen, R; Appleby, R B; Aquines Gutierrez, O; Archilli, F; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Bachmann, S; Back, J J; Baesso, C; Balagura, V; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Bauer, Th; Bay, A; Beddow, J; Bedeschi, F; Bediaga, I; Belogurov, S; Belous, K; Belyaev, I; Ben-Haim, E; Benayoun, M; Bencivenni, G; Benson, S; Benton, J; Berezhnoy, A; Bernet, R; Bettler, M -O; van Beuzekom, M; Bien, A; Bifani, S; Bird, T; Bizzeti, A; Bjørnstad, P M; Blake, T; Blanc, F; Blouw, J; Blusk, S; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borgia, A; Bowcock, T J V; Bowen, E; Bozzi, C; Brambach, T; van den Brand, J; Bressieux, J; Brett, D; Britsch, M; Britton, T; Brook, N H; Brown, H; Burducea, I; Bursche, A; Busetto, G; Buytaert, J; Cadeddu, S; Callot, O; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carranza-Mejia, H; Carson, L; Carvalho Akiba, K; Casse, G; Cattaneo, M; Cauet, Ch; Charles, M; Charpentier, Ph; Chen, P; Chiapolini, N; Chrzaszcz, M; Ciba, K; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coca, C; Coco, V; Cogan, J; Cogneras, E; Collins, P; Comerma-Montells, A; Contu, A; Cook, A; Coombes, M; Coquereau, S; Corti, G; Couturier, B; Cowan, G A; Craik, D; Cunliffe, S; Currie, R; D'Ambrosio, C; David, P; David, P N Y; De Bonis, I; De Bruyn, K; De Capua, S; De Cian, M; De Miranda, J M; De Oyanguren Campos, M; De Paula, L; De Silva, W; De Simone, P; Decamp, D; Deckenhoff, M; Del Buono, L; Derkach, D; Deschamps, O; Dettori, F; Di Canto, A; Dijkstra, H; Dogaru, M; Donleavy, S; Dordei, F; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dupertuis, F; Dzhelyadin, R; Dziurda, A; Dzyuba, A; Easo, S; Egede, U; Egorychev, V; Eidelman, S; van Eijk, D; Eisenhardt, S; Eitschberger, U; Ekelhof, R; Eklund, L; El Rifai, I; Elsasser, Ch; Elsby, D; Falabella, A; Färber, C; Fardell, G; Farinelli, C; Farry, S; Fave, V; Ferguson, D; Fernandez Albor, V; Ferreira Rodrigues, F; Ferro-Luzzi, M; Filippov, S; Fitzpatrick, C; Fontana, M; Fontanelli, F; Forty, R; Francisco, O; Frank, M; Frei, C; Frosini, M; Furcas, S; Furfaro, E; Gallas Torreira, A; Galli, D; Gandelman, M; Gandini, P; Gao, Y; Garofoli, J; Garosi, P; Garra Tico, J; Garrido, L; Gaspar, C; Gauld, R; Gersabeck, E; Gersabeck, M; Gershon, T; Ghez, Ph; Gibson, V; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gordon, H; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graziani, G; Grecu, A; Greening, E; Gregson, S; Grünberg, O; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Hadjivasiliou, C; Haefeli, G; Haen, C; Haines, S C; Hall, S; Hampson, T; Hansmann-Menzemer, S; Harnew, N; Harnew, S T; Harrison, J; Hartmann, T; He, J; Heijne, V; Hennessy, K; Henrard, P; Hernando Morata, J A; van Herwijnen, E; Hicks, E; Hill, D; Hoballah, M; Hombach, C; Hopchev, P; Hulsbergen, W; Hunt, P; Huse, T; Hussain, N; Hutchcroft, D; Hynds, D; Iakovenko, V; Idzik, M; Ilten, P; Jacobsson, R; Jaeger, A; Jans, E; Jaton, P; Jing, F; John, M; Johnson, D; Jones, C R; Jost, B; Kaballo, M; Kandybei, S; Karacson, M; Karbach, T M; Kenyon, I R; Kerzel, U; Ketel, T; Keune, A; Khanji, B; Kochebina, O; Komarov, I; Koopman, R F; Koppenburg, P; Korolev, M; Kozlinskiy, A; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Kucharczyk, M; Kudryavtsev, V; Kvaratskheliya, T; La Thi, V N; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lambert, R W; Lanciotti, E; Lanfranchi, G; Langenbruch, C; Latham, T; Lazzeroni, C; Le Gac, R; van Leerdam, J; Lees, J -P; Lefèvre, R; Leflat, A; Lefrançois, J; Leo, S; Leroy, O; Leverington, B; Li, Y; Li Gioi, L; Liles, M; Lindner, R; Linn, C; Liu, B; Liu, G; von Loeben, J; Lohn, S; Lopes, J H; Lopez Asamar, E; Lopez-March, N; Lu, H; Lucchesi, D; Luisier, J; Luo, H; Machefert, F; Machikhiliyan, I V; Maciuc, F; Maev, O; Malde, S; Manca, G; Mancinelli, G; Marconi, U; Märki, R; Marks, J; Martellotti, G; Martens, A; Martin, L; Martín Sánchez, A; Martinelli, M; Martinez Santos, D; Martins Tostes, D; Massafferri, A; Matev, R; Mathe, Z; Matteuzzi, C; Maurice, E; Mazurov, A; McCarthy, J; McNulty, R; Mcnab, A; Meadows, B; Meier, F; Meissner, M; Merk, M; Milanes, D A; Minard, M -N; Molina Rodriguez, J; Monteil, S; Moran, D; Morawski, P; Morello, M J; Mountain, R; Mous, I; Muheim, F; Müller, K; Muresan, R; Muryn, B; Muster, B; Naik, P; Nakada, T; Nandakumar, R; Nasteva, I; Needham, M; Neufeld, N; Nguyen, A D; Nguyen, T D; Nguyen-Mau, C; Nicol, M; Niess, V; Niet, R; Nikitin, N; Nikodem, T; Nomerotski, A; Novoselov, A; Oblakowska-Mucha, A; Obraztsov, V; Oggero, S; Ogilvy, S; Okhrimenko, O; Oldeman, R; Orlandea, M; Otalora Goicochea, J M; Owen, P; Pal, B K; Palano, A; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Parkes, C; Parkinson, C J; Passaleva, G; Patel, G D; Patel, M; Patrick, G N; Patrignani, C; Pavel-Nicorescu, C; Pazos Alvarez, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perego, D L; Perez Trigo, E; Pérez-Calero Yzquierdo, A; Perret, P; Perrin-Terrin, M; Pessina, G; Petridis, K; Petrolini, A; Phan, A; Picatoste Olloqui, E; Pietrzyk, B; Pilař, T; Pinci, D; Playfer, S; Plo Casasus, M; Polci, F; Polok, G; Poluektov, A; Polycarpo, E; Popov, D; Popovici, B; Potterat, C; Powell, A; Prisciandaro, J; Pugatch, V; Puig Navarro, A; Punzi, G; Qian, W; Rademacker, J H; Rakotomiaramanana, B; Rangel, M S; Raniuk, I; Rauschmayr, N; Raven, G; Redford, S; Reid, M M; dos Reis, A C; Ricciardi, S; Richards, A; Rinnert, K; Rives Molina, V; Roa Romero, D A; Robbe, P; Rodrigues, E; Rodriguez Perez, P; Roiser, S; Romanovsky, V; Romero Vidal, A; Rouvinet, J; Ruf, T; Ruffini, F; Ruiz, H; Ruiz Valls, P; Sabatino, G; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salzmann, C; Sanmartin Sedes, B; Sannino, M; Santacesaria, R; Santamarina Rios, C; Santovetti, E; Sapunov, M; Sarti, A; Satriano, C; Satta, A; Savrie, M; Savrina, D; Schaack, P; Schiller, M; Schindler, H; Schlupp, M; Schmelling, M; Schmidt, B; Schneider, O; Schopper, A; Schune, M -H; Schwemmer, R; Sciascia, B; Sciubba, A; Seco, M; Semennikov, A; Senderowska, K; Sepp, I; Serra, N; Serrano, J; Seyfert, P; Shapkin, M; Shapoval, I; Shatalov, P; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, O; Shevchenko, V; Shires, A; Silva Coutinho, R; Skwarnicki, T; Smith, N A; Smith, E; Smith, M; Sokoloff, M D; Soler, F J P; Soomro, F; Souza, D; Souza De Paula, B; Spaan, B; Sparkes, A; Spradlin, P; Stagni, F; Stahl, S; Steinkamp, O; Stoica, S; Stone, S; Storaci, B; Straticiuc, M; Straumann, U; Subbiah, V K; Swientek, S; Syropoulos, V; Szczekowski, M; Szczypka, P; Szumlak, T; T'Jampens, S; Teklishyn, M; Teodorescu, E; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Tolk, S; Tonelli, D; Topp-Joergensen, S; Torr, N; Tournefier, E; Tourneur, S; Tran, M T; Tresch, M; Tsaregorodtsev, A; Tsopelas, P; Tuning, N; Ubeda Garcia, M; Ukleja, A; Urner, D; Uwer, U; Vagnoni, V; Valenti, G; Vazquez Gomez, R; Vazquez Regueiro, P; Vecchi, S; Velthuis, J J; Veltri, M; Veneziano, G; Vesterinen, M; Viaud, B; Vieira, D; Vilasis-Cardona, X; Vollhardt, A; Volyanskyy, D; Voong, D; Vorobyev, A; Vorobyev, V; Voß, C; Voss, H; Waldi, R; Wallace, R; Wandernoth, S; Wang, J; Ward, D R; Watson, N K; Webber, A D; Websdale, D; Whitehead, M; Wicht, J; Wiechczynski, J; Wiedner, D; Wiggers, L; Wilkinson, G; Williams, M P; Williams, M; Wilson, F F; Wishahi, J; Witek, M; Wotton, S A; Wright, S; Wu, S; Wyllie, K; Xie, Y; Xing, F; Xing, Z; Yang, Z; Young, R; Yuan, X; Yushchenko, O; Zangoli, M; Zavertyaev, M; Zhang, F; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhokhov, A; Zhong, L; Zvyagin, A

2013-01-01

The decay $B_c^+ \\to \\psi(2S)\\pi^+$ with $\\psi(2S) \\to \\mu^+\\mu^-$ is observed with a significance of $5.2\\,\\sigma$ using $pp$ collision data corresponding to an integrated luminosity of 1.0 $fb^{-1}$ collected by the LHCb experiment. The branching fraction of $B_c^+ \\to \\psi(2S)\\pi^+$ decays relative to that of the $B_c^+ \\to J/\\psi\\pi^+$ mode is measured to be \\begin{equation*} \\frac{\\mathcal{B}(B_c^+ \\to \\psi(2S)\\pi^+)}{\\mathcal{B}(B_c^+ \\to J/\\psi\\pi^+)} = 0.250 \\pm 0.068 \\,\\text{stat} \\pm 0.014 \\,\\text{\\syst} \\pm 0.006 \\,(\\mathcal{B}). \\end{equation*} The last term is the uncertainty on the ratio $\\mathcal{B}(\\psi(2S) \\to \\mu^+\\mu^-)/\\mathcal{B}(J/\\psi \\to \\mu^+\\mu^-)$.

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...

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

The Sneaky Sneaker Spies and the Mysterious Black Ink

Science.gov (United States)

Savran, Michelle

2012-01-01

In this article, the author describes the process of making "The Sneaky Sneaker Spies and the Mysterious Black Ink," a six-minute animation starring five art students who form a detective club. This animation is available online for art teachers to use in their own classrooms. After showing this video in class, art teachers could have students try…

Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI)

Science.gov (United States)

Asadi Zarch, Mohammad Amin; Sivakumar, Bellie; Sharma, Ashish

2015-07-01

RDI. The results suggest that agreement between SPI and RDI is affected and decreases remarkably over time (between 1850 and 2100). All these lead to the conclusion that, in the face of climate change, PET, an important component in the hydrologic cycle, should not be ignored in drought modeling.

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 Impact of Female Attractiveness in Spy

OpenAIRE

Pangjaya, Veronika Juliani; Handojo, Priska Febrinia

2017-01-01

This thesis discusses the impact of female attractiveness on the female character which is related to the way Spy sees attractive women and what the impacts to them are. There are criteria for female attractiveness that are displayed by the film such as physical attributes and traits. In order to show the criteria of attractive women and the responses that they get, I use stereotyping theory. Women who are physically attractive get better treatment, but in order to get that, they have to dres...

State Transition and Flaring Activity of IGR J17464-3213/H1743-322 with INTEGRAL SPI

Science.gov (United States)

Joinet, A.; Jourdain, E.; Malzac, J.; Roques, J. P.; Schönfelder, V.; Ubertini, P.; Capitanio, F.

2005-08-01

IGR J17464-3213, already known as the HEAO 1 transient source H1743-322, has been detected during a state transition by INTEGRAL SPI. We describe the spectral evolution and flaring activity of IGR J17464-3213/H1743-322 from 2003 March 21 to 2003 April 22. During the first part, the source followed a continuous spectral softening, with the peak of the spectral energy distribution shifting from 100 keV down to ~a few keV. However, the thermal disk and the hard X-ray components had a similar intensity, indicating that the source was in an intermediate state throughout our observations and evolving toward the soft state. In the second part of our observations, the RXTE ASM and INTEGRAL SPI light curves showed a strong flaring activity. Two flare events lasting about 1 day each have been detected with SPI and are probably due to instabilities in the accretion disk associated with the state transition. During these flares, the low (1.5-12 keV) and high (20-200 keV) energy fluxes monitored with the RXTE ASM and INTEGRAL SPI are correlated, and the spectral shape (above 20 keV) remains unchanged while the luminosity increases by a factor greater than 2.

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.

Spin echo SPI methods for quantitative analysis of fluids in porous media.

Science.gov (United States)

Li, Linqing; Han, Hui; Balcom, Bruce J

2009-06-01

Fluid density imaging is highly desirable in a wide variety of porous media measurements. The SPRITE class of MRI methods has proven to be robust and general in their ability to generate density images in porous media, however the short encoding times required, with correspondingly high magnetic field gradient strengths and filter widths, and low flip angle RF pulses, yield sub-optimal S/N images, especially at low static field strength. This paper explores two implementations of pure phase encode spin echo 1D imaging, with application to a proposed new petroleum reservoir core analysis measurement. In the first implementation of the pulse sequence, we modify the spin echo single point imaging (SE-SPI) technique to acquire the k-space origin data point, with a near zero evolution time, from the free induction decay (FID) following a 90 degrees excitation pulse. Subsequent k-space data points are acquired by separately phase encoding individual echoes in a multi-echo acquisition. T(2) attenuation of the echo train yields an image convolution which causes blurring. The T(2) blur effect is moderate for porous media with T(2) lifetime distributions longer than 5 ms. As a robust, high S/N, and fast 1D imaging method, this method will be highly complementary to SPRITE techniques for the quantitative analysis of fluid content in porous media. In the second implementation of the SE-SPI pulse sequence, modification of the basic measurement permits fast determination of spatially resolved T(2) distributions in porous media through separately phase encoding each echo in a multi-echo CPMG pulse train. An individual T(2) weighted image may be acquired from each echo. The echo time (TE) of each T(2) weighted image may be reduced to 500 micros or less. These profiles can be fit to extract a T(2) distribution from each pixel employing a variety of standard inverse Laplace transform methods. Fluid content 1D images are produced as an essential by product of determining the

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...

[Assisted reproductive medicine in Poland, 2011--SPiN PTG report].

Science.gov (United States)

Janicka, Anna; Spaczyński, Robert Z; Kurzawa, Rafał

2014-07-01

The aim of this report is to present data concerning results and complications related to infertility treatment using assisted reproductive technology (ART) and insemination (IUI) in Poland in 2011. The report was prepared by the Fertility and Sterility Special Interest Group of the Polish Gynaecological Society (SPiN PTG), based on individual data provided by fertility clinics in Poland. Reporting was voluntary and the provided data was not subject to external control. The report presents the availability and the structure of infertility treatment services, the number of procedures performed, their effectiveness and the most common complications. In 2013, 33 Polish fertility clinics provided information to the SPiN PTG report, presenting data from the year 2011. The total number of reported treatment cycles using ART was 15,340 (incl. 10,011 IVF/ICSI procedures) and 15,627 IUI procedures. The rate of clinical pregnancies in terms of a cycle was 34.2% in case of IVF/ ICSI procedures and 13.4% in case of IUI. The prevalence of multiple births was 20.2% and 8.3% respectively in case of IVF/ICSI and IUI methods. The most frequent complication in the course of treatment using ART was ovarian hyperstimulation syndrome (OHSS). The SPiN PTG report allows to find out the average effectiveness and safety of assisted reproduction technologies and is currently the only proof of responsibility and due diligence of fertility centres in Poland. However due to the lack of a central register of fertility clinics, facultative participation in the report as well as incomplete information on pregnancy and delivery the collected data does not reflect the full spectrum of the Polish reproductive medicine.

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.

Spatial and temporal analysis of drought variability at several time scales in Syria during 1961-2012

Science.gov (United States)

Mathbout, Shifa; Lopez-Bustins, Joan A.; Martin-Vide, Javier; Bech, Joan; Rodrigo, Fernando S.

2018-02-01

This paper analyses the observed spatiotemporal characteristics of drought phenomenon in Syria using the Standardised Precipitation Index (SPI) and the Standardised Precipitation Evapotranspiration Index (SPEI). Temporal variability of drought is calculated for various time scales (3, 6, 9, 12, and 24 months) for 20 weather stations over the 1961-2012 period. The spatial patterns of drought were identified by applying a Principal Component Analysis (PCA) to the SPI and SPEI values at different time scales. The results revealed three heterogeneous and spatially well-defined regions with different temporal evolution of droughts: 1) Northeastern (inland desert); 2) Southern (mountainous landscape); 3) Northwestern (Mediterranean coast). The evolutionary characteristics of drought during 1961-2012 were analysed including spatial and temporal variability of SPI and SPEI, the frequency distribution, and the drought duration. The results of the non-parametric Mann-Kendall test applied to the SPI and SPEI series indicate prevailing significant negative trends (drought) at all stations. Both drought indices have been correlated both on spatial and temporal scales and they are highly comparable, especially, over a 12 and 24 month accumulation period. We concluded that the temporal and spatial characteristics of the SPI and SPEI can be used for developing a drought intensity - areal extent - and frequency curve that assesses the variability of regional droughts in Syria. The analysis of both indices suggests that all three regions had a severe drought in the 1990s, which had never been observed before in the country. Furthermore, the 2007-2010 drought was the driest period in the instrumental record, happening just before the onset of the recent conflict in Syria.

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

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...

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...

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.

Product focused software process improvement (P-SPI) : concepts and their application

NARCIS (Netherlands)

Solingen, van D.M.; Kusters, R.J.; Trienekens, J.J.M.; Uijtrecht, van A.

1999-01-01

Management problems in the development of software have been addressed over recent years by a focus on improvement of the development process. This paper states that software process improvement (SPI) should have an explicit product focus. The practical implementation of a method for product-focused

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...

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.

CWI cryptanalyst discovers new cryptographic attack variant in Flame spy malware

NARCIS (Netherlands)

M.M.J. Stevens (Marc); R.J.F. Cramer (Ronald)

2012-01-01

htmlabstractCryptanalyst Marc Stevens from the Centrum Wiskunde & Informatica (CWI) in Amsterdam, known for breaking the https security in 2008 using a cryptanalytic attack on MD5, analyzed the recent Flame virus this week. He discovered that for this spy malware an as yet unknown cryptographic

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.

[Nurses and spies during the First World War].

Science.gov (United States)

Halioua, Bruno

2014-06-01

During the First World War, some nurses distinguished themselves by playing a significant role in spy networks, using their activity as a cover. They took an active part in the setting up of escape routes for allied prisoners of war and the gathering of intelligence on the positions of German troops, in particular in Belgium and northern France. Among them Edith Cavell, Gabrielle Petit, Louise de Bettignies, Marie-Léonie Vanhoutte, Marthe Cocknaert and Emilienne-Rose Ducimetière are considered as heroines.

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

SPY: a new scission-point model based on microscopic inputs to predict fission fragment properties

Energy Technology Data Exchange (ETDEWEB)

Panebianco, Stefano; Lemaître, Jean-Francois; Sida, Jean-Luc [CEA Centre de Saclay, Gif-sur-Ivette (France); Dubray, Noëel [CEA, DAM, DIF, Arpajon (France); Goriely, Stephane [Institut d' Astronomie et d' Astrophisique, Universite Libre de Bruxelles, Brussels (Belgium)

2014-07-01

Despite the difficulty in describing the whole fission dynamics, the main fragment characteristics can be determined in a static approach based on a so-called scission-point model. Within this framework, a new Scission-Point model for the calculations of fission fragment Yields (SPY) has been developed. This model, initially based on the approach developed by Wilkins in the late seventies, consists in performing a static energy balance at scission, where the two fragments are supposed to be completely separated so that their macroscopic properties (mass and charge) can be considered as fixed. Given the knowledge of the system state density, averaged quantities such as mass and charge yields, mean kinetic and excitation energy can then be extracted in the framework of a microcanonical statistical description. The main advantage of the SPY model is the introduction of one of the most up-to-date microscopic descriptions of the nucleus for the individual energy of each fragment and, in the future, for their state density. These quantities are obtained in the framework of HFB calculations using the Gogny nucleon-nucleon interaction, ensuring an overall coherence of the model. Starting from a description of the SPY model and its main features, a comparison between the SPY predictions and experimental data will be discussed for some specific cases, from light nuclei around mercury to major actinides. Moreover, extensive predictions over the whole chart of nuclides will be discussed, with particular attention to their implication in stellar nucleosynthesis. Finally, future developments, mainly concerning the introduction of microscopic state densities, will be briefly discussed. (author)

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.

Comparing Image Perception of Bladder Tumors in Four Different Storz Professional Image Enhancement System Modalities Using the íSPIES App

NARCIS (Netherlands)

Kamphuis, Guido M.; de Bruin, D. Martijn; Brandt, Martin J.; Knoll, Thomas; Conort, Pierre; Lapini, Alberto; Dominguez-Escrig, Jose L.; de La Rosette, Jean J. M. C. H.

2016-01-01

To evaluate the variation of interpretation of the same bladder urothelium image in different Storz Professional Image Enhancement System (SPIES) modalities. SPIES contains a White light (WL), Spectra A (SA), Spectra B (SB), and Clara and Chroma combined (CC) modality. An App for the iPAD retina was

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

Comparing the Palmer Drought Index and the Standardized Precipitation Index for Zagreb-Gric Observatory

Science.gov (United States)

Pandzic, Kreso

2016-04-01

Conventional Palmer Drought Index (PDSI) and recent Standardized Precipitation Index (SPI) are compared for Zagreb-Gric weather station. Historical time series of PDSI and SPI are compared. For that purpose monthly precipitation, air temperature and air humidity data for Zagreb-Gric Observatory and period 1862-2012 are used. The results indicate that SPI is simpler for interpretation than PDSI. On the other side, lack of temperature within SPI, make impossible use of it on climate change applications. A comparison of PDSI and SPI for the periods from 1 to 24 months indicate the best agreement between PDSI and SPI for the periods from 6 to 12 months. In addition, correlation coefficients of determination between annual corn crop per hectare and SPI 9- months time scale and PDSI from May to October are shown as significant.

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

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.

Understanding surveillance technologies spy devices, their origins & applications

CERN Document Server

Petersen, JK

2001-01-01

From electronic wire taps to baby monitors and long-distance video and listening devices, startling changes occur everyday in how we gather, interpret, and transmit information. An extraordinary range of powerful new technologies has come into existence to meet the requirements of this expanding field.Your search for a comprehensive resource for surveillance devices is over. Understanding Surveillance Technologies: Spy Devices, Their Origins and Applications serves as a provocative, broad-based, and visually appealing reference that introduces and describes the technologies rapidly moving into

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.

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.

SPYING FOR THE RIGHT REASONS: CONTESTED NORMS IN CYBERSPACE

Science.gov (United States)

2017-04-06

Understanding international law is difficult because it is based upon a different philosophy than domestic law. Domestic laws in Western democracies are very...relations and protects nations from a hostile intent.24 In summary, this essay is based on the assumption that espionage is legitimate in legal and moral ...espionage from EMCE.32 It appears that the moral aspects of spying among allies are hardly researched at all. Leif-Eric Easley examined the factor of

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.

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.

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...

Test-retest reliability and agreement of the SPI-Questionnaire to detect symptoms of digital ischemia in elite volleyball players.

Science.gov (United States)

van de Pol, Daan; Zacharian, Tigran; Maas, Mario; Kuijer, P Paul F M

2017-06-01

The Shoulder posterior circumflex humeral artery Pathology and digital Ischemia - questionnaire (SPI-Q) has been developed to enable periodic surveillance of elite volleyball players, who are at risk for digital ischemia. Prior to implementation, assessing reliability is mandatory. Therefore, the test-retest reliability and agreement of the SPI-Q were evaluated among the population at risk. A questionnaire survey was performed with a 2-week interval among 65 elite male volleyball players assessing symptoms of cold, pale and blue digits in the dominant hand during or after practice or competition using a 4-point Likert scale (never, sometimes, often and always). Kappa (κ) and percentage of agreement (POA) were calculated for individual symptoms, and to distinguish symptomatic and asymptomatic players. For the individual symptoms, κ ranged from "poor" (0.25) to "good" (0.63), and POA ranged from "moderate" (78%) to "good" (97%). To classify symptomatic players, the SPI-Q showed "good" reliability (κ = 0.83; 95%CI 0.69-0.97) and "good" agreement (POA = 92%). The current study has proven the SPI-Q to be reliable for detecting elite male indoor volleyball players with symptoms of digital ischemia.

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.

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...

Minox Digital Spy Camera数码相机

Institute of Scientific and Technical Information of China (English)

无

2009-01-01

Digital Spy Camera是一种间谍框机，看起来更像是一个小MP3，重量只有60g，非常便携．非常迷你，内置320万像素cmos传感器．42mm定焦镜头最大光圈130．还有一个外接的闪光灯．其外形尺寸86mm×29mm×20mm．呈现细长形结构．

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.

Pre Cold War British Spy Fiction, the “albatross of self” and lines of flight in Gravity’s Rainbow.

Directory of Open Access Journals (Sweden)

Kyle Wishart Smith

2015-08-01

Full Text Available In his introduction to 'Slow Learner' Thomas Pynchon suggests that an influence in his short story ‘Under the Rose’ was the spy fiction he had read as a child.  What he takes from the form, he says, is an enjoyment of  “lurking, spying, false identities, psychological games.” I hope to show that this youthful reading has interesting things to tell us about Pynchon’s writing beyond ‘Under the Rose’ and in more complex ways than his quote suggests. To do this I want to focus on that perennial issue of spy fiction - the maintenance and manipulation of identity. Negotiating ideas of subjectivity is a core concern in Pynchon’s work and to consider it I want to use the four spy novelists he mentions in the 'Slow Learner' introduction - John Buchan, E. Phillips Oppenheim, Helen MacInnes and Geoffrey Household. This is a more disparate quartet of authors than Pynchon’s grouping suggests and I want to employ them to consider a variety of strategies used to ‘build character’ and the way Pynchon’s work approaches these strategies.  This allows a reflection on questions of disguise, doubles, animals and the nomad within the context of a variety of postcolonial theories and aspects of Deleuze and Guattari’s “nomadology”. 'V 'would appear an obvious place to see connections to spy fiction, but, though I touch on some aspects of this novel, my focus will be very much on 'Gravity’s Rainbow' because it has a much more concerted focus on the subject of Empire. Some intriguing echoes are to be found in the work of Pynchon in these authors and I hope to show how Pynchon’s attempts to formulate US “superimperialism” (Aijaz Ahmad are reflected in the imperial concerns of what I would term the pre-Cold War British Spy fiction that engaged Pynchon in his youth.

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.

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.

A novel approach for modelling the cluster detector and the SPI ...

Indian Academy of Sciences (India)

detectors and for performing experimental studies with the SPI spectrometer for high-energy γ -rays. Keywords. ... corresponds to the complete absorption of incident γ energy and gets contributions from. Pramana – J. Phys. ..... For a γ-ray of energy Eγ , let us first define the ratio of the single-detector hit events to the total full ...

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").

Chemical Method to Improve CO{sub 2} Flooding Sweep Efficiency for Oil Recovery Using SPI-CO{sub 2} Gels

Energy Technology Data Exchange (ETDEWEB)

Burns, Lyle D.

2009-04-14

The problem in CO{sub 2} flooding lies with its higher mobility causing low conformance or sweep efficiency. This is an issue in oilfield applications where an injected fluid or gas used to mobilize and produce the oil in a marginal field has substantially higher mobility (function of viscosity and density and relative permeability) relative to the crude oil promoting fingering and early breakthrough. Conformance is particularly critical in CO{sub 2} oilfield floods where the end result is less oil recovered and substantially higher costs related to the CO{sub 2}. The SPI-CO{sub 2} (here after called “SPI”) gel system is a unique silicate based gel system that offers a technically effective solution to the conformance problem with CO{sub 2} floods. This SPI gel system remains a low viscosity fluid until an external initiator (CO{sub 2}) triggers gelation. This is a clear improvement over current technologies where the gels set up as a function of time, regardless of where it is placed in the reservoir. In those current systems, the internal initiator is included in the injected fluid for water shut off applications. In this new research effort, the CO{sub 2} is an external initiator contacted after SPI gel solution placement. This concept ensures in the proper water wet reservoir environment that the SPI gel sets up in the precise high permeability path followed by the CO{sub 2}, therefore improving sweep efficiency to a greater degree than conventional systems. In addition, the final SPI product in commercial quantities is expected to be low cost over the competing systems. This Phase I research effort provided “proof of concept” that SPI gels possess strength and may be formed in a sand pack reducing the permeability to brine and CO{sub 2} flow. This SPI technology is a natural extension of prior R & D and the Phase I effort that together show a high potential for success in a Phase II follow-on project. Carbon dioxide (CO{sub 2}) is a major by-product of

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.

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.

INTEGRAL IBIS, SPI, and JEM-X observations of LVT151012

DEFF Research Database (Denmark)

Savchenko, V.; Bazzano, A.; Bozzo, E.

2017-01-01

favorable location of the counterpart for a detection by the omni-directionalinstruments. These results can be interpreted as a tight constrain on the ratio of the isotropic equivalent energy releasedin the electromagnetic emission to the total energy of the gravitational waves: E75−2000 keV /EGW ...During the first observing run of LIGO, two gravitational wave events and one lower-significance trigger (LVT151012) were reported by the LIGO/Virgo collaboration. At the time of LVT151012, the INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL) was pointing at a region of the sky coincident...... with the high localization probability area of the event and thus permitted us to search for its electromagnetic counterpart (both prompt and afterglow emission). The imaging instruments on-board INTEGRAL (IBIS/ISGRI, IBIS/PICsIT, SPI, and the two JEM-X modules)have been exploited to attempt the detection...

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.

Characterization of the SPI-1 and Rsp type three secretion systems in Pseudomonas fluorescens F113.

Science.gov (United States)

Barret, Matthieu; Egan, Frank; Moynihan, Jennifer; Morrissey, John P; Lesouhaitier, Olivier; O'Gara, Fergal

2013-06-01

Pseudomonas fluorescens F113 is a plant growth-promoting rhizobacterium (PGPR) isolated from the sugar beet rhizosphere. The recent annotation of the F113 genome sequence has revealed that this strain encodes a wide array of secretion systems, including two complete type three secretion systems (T3SSs) belonging to the Hrp1 and SPI-1 families. While Hrp1 T3SSs are frequently encoded in other P. fluorescens strains, the presence of a SPI-1 T3SS in a plant-beneficial bacterial strain was unexpected. In this work, the genetic organization and expression of these two T3SS loci have been analysed by a combination of transcriptional reporter fusions and transcriptome analyses. Overexpression of two transcriptional activators has shown a number of genes encoding putative T3 effectors. In addition, the influence of these two T3SSs during the interaction of P. fluorescens F113 with some bacterial predators was also assessed. Our data revealed that the transcriptional activator hilA is induced by amoeba and that the SPI-1 T3SS could potentially be involved in resistance to amoeboid grazing. © 2013 John Wiley & Sons Ltd and Society for Applied Microbiology.

Drought Forecasting by SPI Index and ANFIS Model Using Fuzzy C-mean Clustering

Directory of Open Access Journals (Sweden)

mehdi Komasi

2013-08-01

Full Text Available Drought is the interaction between environment and water cycle in the world and affects natural environment of an area when it persists for a longer period. So, developing a suitable index to forecast the spatial and temporal distribution of drought plays an important role in the planning and management of natural resources and water resource systems. In this article, firstly, the drought concept and drought indexes were introduced and then the fuzzy neural networks and fuzzy C-mean clustering were applied to forecast drought via standardized precipitation index (SPI. The results of this research indicate that the SPI index is more capable than the other indexes such as PDSI (Palmer Drought Severity Index, PAI (Palfai Aridity Index and etc. in drought forecasting process. Moreover, application of adaptive nero-fuzzy network accomplished by C-mean clustering has high efficiency in the drought forecasting.

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.

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.

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.

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...

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…

Empirical method to measure stochasticity and multifractality in nonlinear time series

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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.

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

The zebrafish spi1 promoter drives myeloid-specific expression in stable transgenic fish

NARCIS (Netherlands)

Ward, AC; McPhee, DO; Condron, MM; Varma, S; Cody, SH; Onnebo, SMN; Paw, BH; Zon, LI; Lieschke, GJ

2003-01-01

The spi1 (pu.1) gene has recently been identified as a useful marker of early myeloid cells in zebrafish. To enhance the versatility of this organism as a model for studying myeloid development, the promoter of this gene has been isolated and characterized. Transient transgenesis revealed that a 5.3

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

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.

Stochastic time series analysis of hydrology data for water resources

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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.

MEJORA DE PROCESOS DE SOFTWARE ÁGIL CON AGILE - SPI PROCESS

Directory of Open Access Journals (Sweden)

CÉSAR PARDO

2010-01-01

Full Text Available Motivar la aplicación de proyectos de mejora de procesos en las empresas de desarrollo de software iberoamericanas, en su gran mayoría Micro, Pequeñas y Medianas Empresas Desarrolladoras de Software (MiPyMEs_DS es una necesidad imperante para la búsqueda de una industria de software que sea competitiva no solo en contextos regionales sino también internacionales. Los modelos internacionalmente reconocidos, representan alto riesgo en su aplicación para una MiPyMES_DS, esto debido quizá a su gran inversión en dinero, tiempo, recursos, difícil gestión, además de la complejidad de las recomendaciones y un retorno de la inversión a largo plazo. El objetivo de este trabajo es presentar a Agile SPI - Process como un proceso de mejora de procesos basado principalmente en metodologías y principios ágiles, requerimientos livianos y adaptaciones de modelos internacionales. De la misma manera en el artículo se presentan los resultados obtenidos en la implementación de Agile SPI - Process en varias MiPyMEs_DS de Iberoamérica y el sur occidente de Colombia.

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.

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.

Expression of OsSPY and 14-3-3 genes involved in plant height variations of ion-beam-induced KDML 105 rice mutants

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Phanchaisri, Boonrak [Science and Technology Research Institute, Chiang Mai University, Chiang Mai 50200 (Thailand); Molecular Biology Laboratory, Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200 (Thailand); Samsang, Nuananong [Molecular Biology Laboratory, Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200 (Thailand); Yu, Liang Deng; Singkarat, Somsorn [Plasma and Beam Physics Research Facility, Department of Physics and Materials Science, Faculty of Science, Chiang Mai University, Chiang Mai 50200 (Thailand); Thailand Center of Excellence in Physics, Commission on Higher Education, 328 Si Ayutthaya Road, Bangkok 10400 (Thailand); Anuntalabhochai, Somboon, E-mail: soanu.1@gmail.com [Molecular Biology Laboratory, Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai 50200 (Thailand)

2012-06-01

The culm length of two semidwarf rice mutants (PKOS1, HyKOS1) obtained from low-energy N-ion beam bombardments of dehusked Thai jasmine rice (Oryza sativa L. cv. KDML 105) seeds showed 25.7% and 21.5% height reductions and one spindly rice mutant (TKOS4) showed 21.4% increase in comparison with that of the KDML 105 control. A cDNA-RAPD analysis identified differential gene expression in internode tissues of the rice mutants. Two genes identified from the cDNA-RAPD were OsSPY and 14-3-3, possibly associated with stem height variations of the semidwarf and spindly mutants, respectively. The OsSPY gene encoded the SPY protein which is considered to be a negative regulator of gibberellin (GA). On the other hand, the 14-3-3 encoded a signaling protein which can bind and prevent the RSG (repression of shoot growth) protein function as a transcriptional repressor of the kaurene oxidase (KO) gene in the GA biosynthetic pathway. Expression analysis of OsSPY, 14-3-3, RSG, KO, and SLR1 was confirmed in rice internode tissues during the reproductive stage of the plants by semi-quantitative RT-PCR technique. The expression analysis showed a clear increase of the levels of OsSPY transcripts in PKOS1 and HyKOS1 tissue samples compared to that of the KDML 105 and TKOS4 samples at the age of 50-60 days which were at the ages of internode elongation. The 14-3-3 expression had the highest increase in the TKOS4 samples compared to those in KDML 105, PKOS1 and HyKOS1 samples. The expression analysis of RSG and KO showed an increase in TKOS4 samples compared to that of the KDML 105 and that of the two semidwarf mutants. These results indicate that changes of OsSPY and 14-3-3 expression could affect internode elongation and cause the phenotypic changes of semidwarf and spindly rice mutants, respectively.

Expression of OsSPY and 14-3-3 genes involved in plant height variations of ion-beam-induced KDML 105 rice mutants

International Nuclear Information System (INIS)

Phanchaisri, Boonrak; Samsang, Nuananong; Yu, Liang Deng; Singkarat, Somsorn; Anuntalabhochai, Somboon

2012-01-01

The culm length of two semidwarf rice mutants (PKOS1, HyKOS1) obtained from low-energy N-ion beam bombardments of dehusked Thai jasmine rice (Oryza sativa L. cv. KDML 105) seeds showed 25.7% and 21.5% height reductions and one spindly rice mutant (TKOS4) showed 21.4% increase in comparison with that of the KDML 105 control. A cDNA-RAPD analysis identified differential gene expression in internode tissues of the rice mutants. Two genes identified from the cDNA-RAPD were OsSPY and 14-3-3, possibly associated with stem height variations of the semidwarf and spindly mutants, respectively. The OsSPY gene encoded the SPY protein which is considered to be a negative regulator of gibberellin (GA). On the other hand, the 14-3-3 encoded a signaling protein which can bind and prevent the RSG (repression of shoot growth) protein function as a transcriptional repressor of the kaurene oxidase (KO) gene in the GA biosynthetic pathway. Expression analysis of OsSPY, 14-3-3, RSG, KO, and SLR1 was confirmed in rice internode tissues during the reproductive stage of the plants by semi-quantitative RT-PCR technique. The expression analysis showed a clear increase of the levels of OsSPY transcripts in PKOS1 and HyKOS1 tissue samples compared to that of the KDML 105 and TKOS4 samples at the age of 50–60 days which were at the ages of internode elongation. The 14-3-3 expression had the highest increase in the TKOS4 samples compared to those in KDML 105, PKOS1 and HyKOS1 samples. The expression analysis of RSG and KO showed an increase in TKOS4 samples compared to that of the KDML 105 and that of the two semidwarf mutants. These results indicate that changes of OsSPY and 14-3-3 expression could affect internode elongation and cause the phenotypic changes of semidwarf and spindly rice mutants, respectively.

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

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

Monitoring and quantifying future climate projections of dryness and wetness extremes: SPI bias

Directory of Open Access Journals (Sweden)

F. Sienz

2012-07-01

Full Text Available The adequacy of the gamma distribution (GD for monthly precipitation totals is reconsidered. The motivation for this study is the observation that the GD fails to represent precipitation in considerable areas of global observed and simulated data. This misrepresentation may lead to erroneous estimates of the Standardised Precipitation Index (SPI, evaluations of models, and assessments of climate change. In this study, the GD is compared to the Weibull (WD, Burr Type III (BD, exponentiated Weibull (EWD and generalised gamma (GGD distribution. These distributions extend the GD in terms of possible shapes (skewness and kurtosis and the behaviour for large arguments. The comparison is based on the Akaike information criterion, which maximises information entropy and reveals a trade-off between deviation and the numbers of parameters used. We use monthly sums of observed and simulated precipitation for 12 calendar months of the year. Assessing observed and simulated data, (i the Weibull type distributions give distinctly improved fits compared to the GD and (ii the SPI resulting from the GD overestimates (underestimates extreme dryness (wetness.

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.

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.

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.

SpiCAD: Integrated environment for circuitry simulation with SPICE code

Energy Technology Data Exchange (ETDEWEB)

D' Amore, D; Padovini, G; Santomauro, M [Politecnico di Milano (Italy). Dip. di Elettronica

1991-11-01

SPICE is one of the most commonly used programs for the simulation of the behaviour of electronic circuits. This article describes in detail the key design characteristics and capabilities of a computer environment called SpiCAD which integrates all the different phases of SPICE based circuitry simulation on a personal computer, i.e., the tracing of the electronics scheme, simulation and visualization of the results so as to help define semiconductor device models, determine input signals, construct macro-models and convert design sketches into formats acceptable to graphic systems.

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 influence of shrinkage-cracking on the drying behaviour of White Portland cement using Single-Point Imaging (SPI).

Science.gov (United States)

Beyea, S D; Balcom, B J; Bremner, T W; Prado, P J; Cross, A R; Armstrong, R L; Grattan-Bellew, P E

1998-11-01

The removal of water from pores in hardened cement paste smaller than 50 nm results in cracking of the cement matrix due to the tensile stresses induced by drying shrinkage. Cracks in the matrix fundamentally alter the permeability of the material, and therefore directly affect the drying behaviour. Using Single-Point Imaging (SPI), we obtain one-dimensional moisture profiles of hydrated White Portland cement cylinders as a function of drying time. The drying behaviour of White Portland cement, is distinctly different from the drying behaviour of related concrete materials containing aggregates.

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.

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…

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.

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 ...

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.

Characteristics and drivers of drought in Europe-a summary of the DROUGHT-R&SPI project

NARCIS (Netherlands)

Tallaksen, Lena M.; Stagge, James H.; Stahl, Kerstin; Gudmundsson, Lukas; Orth, Rene; Seneviratne, Sonia I.; Loon, van Anne F.; Lanen, van Henny A.J.

2015-01-01

A prerequisite to mitigate the wide range of drought impacts is to establish a good understanding of the drought generating mechanisms from their initiation as a meteorological drought through to their development as soil moisture and hydrological drought. The DROUGHT-R&SPI project has

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

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...

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.

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.

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 ...

Rainfall characterisation by application of standardised precipitation index (SPI) in Peninsular Malaysia

Science.gov (United States)

Yusof, Fadhilah; Hui-Mean, Foo; Suhaila, Jamaludin; Yusop, Zulkifli; Ching-Yee, Kong

2014-02-01

The interpretations of trend behaviour for dry and wet events are analysed in order to verify the dryness and wetness episodes. The fitting distribution of rainfall is computed to classify the dry and wet events by applying the standardised precipitation index (SPI). The rainfall amount for each station is categorised into seven categories, namely extremely wet, severely wet, moderately wet, near normal, moderately dry, severely dry and extremely dry. The computation of the SPI is based on the monsoon periods, which include the northeast monsoon, southwest monsoon and inter-monsoon. The trends of the dry and wet periods were then detected using the Mann-Kendall trend test and the results indicate that the major parts of Peninsular Malaysia are characterised by increasing droughts rather than wet events. The annual trends of drought and wet events of the randomly selected stations from each region also yield similar results. Hence, the northwest and southwest regions are predicted to have a higher probability of drought occurrence during a dry event and not much rain during the wet event. The east and west regions, on the other hand, are going through a significant upward trend that implies lower rainfall during the drought episodes and heavy rainfall during the wet events.

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

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

Markov Trends in Macroeconomic Time Series

NARCIS (Netherlands)

R. Paap (Richard)

1997-01-01

textabstractMany macroeconomic time series are characterised by long periods of positive growth, expansion periods, and short periods of negative growth, recessions. A popular model to describe this phenomenon is the Markov trend, which is a stochastic segmented trend where the slope depends on the

Modeling seasonality in bimonthly time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans)

1992-01-01

textabstractA recurring issue in modeling seasonal time series variables is the choice of the most adequate model for the seasonal movements. One selection method for quarterly data is proposed in Hylleberg et al. (1990). Market response models are often constructed for bimonthly variables, and

On clustering fMRI time series

DEFF Research Database (Denmark)

Goutte, Cyril; Toft, Peter Aundal; Rostrup, E.

1999-01-01

Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength. However, these methods do...

Índice estandarizado de precipitación (SPI para la caracterización de sequías meteorológicas en la cuenca del río Dagua-Colombia

Directory of Open Access Journals (Sweden)

Loaiza Cerón, Wilmar

2015-12-01

Full Text Available The Standardized Precipitation Index (SPI characterizes meteorological droughts of a place, by quantifying the deficit of rainfall in different time periods. Negative SPI values represents drought, while positive values indicate wet periods. The results suggest that negative SPI periods coincide with El Niño years: 82-83, 91-92 and 97-98. The 91-92 event had the largest impact on the region. These results indicate that the most severe droughts in the basin of Dagua, were presented at the quarters of Dec- Jan-Feb, Jan-Feb-Mar, Jun-Jul-Aug and Jul-Aug-Sep. The results have been obtained with geostatistical techniques, and are a basic input to formulate adaptation strategies and ensure food sovereignty of small farmers against drought.El Índice Estandarizado de Precipitación (SPI caracteriza las sequías meteorológicas de un lugar, mediante la cuantificación del déficit de precipitación en diferentes períodos de tiempo. Valores negativos del SPI representan sequías, mientras valores positivos indican periodos húmedos. Los resultados muestran que los períodos SPI negativos, coinciden con años clasificados como El Niño: 82-83, 91-92 y 97-98, de los cuales, el evento 91-92 presentó los mayores impactos en la región. En general, los resultados indican que las sequías más intensas en la cuenca del Dagua, se presentaron en los trimestres de Dic-Ene-Feb, Ene-Feb-Mar, Jun-Jul-Ago y Jul-Ago-Sep; estos resultados se espacializaron con técnicas geoestadísticas, y son insumo básico para formular estrategias de adaptación y garantizar la soberanía alimentaria de los pequeños agricultores frente a las sequías. [fr] L’Indice de précipitations normalisé (SPI caractérise les sécheresses météorologiques d’un lieu, en quantifiant le déficit de précipitations à différentes périodes de temps. Les valeurs négatives de SPI indiquent périodes sécheresse, tandis que les positives indiquent des périodes humides. Les r

FALSE DETERMINATIONS OF CHAOS IN SHORT NOISY TIME SERIES. (R828745)

Science.gov (United States)

A method (NEMG) proposed in 1992 for diagnosing chaos in noisy time series with 50 or fewer observations entails fitting the time series with an empirical function which predicts an observation in the series from previous observations, and then estimating the rate of divergenc...

Multiscale multifractal multiproperty analysis of financial time series based on Rényi entropy

Science.gov (United States)

Yujun, Yang; Jianping, Li; Yimei, Yang

This paper introduces a multiscale multifractal multiproperty analysis based on Rényi entropy (3MPAR) method to analyze short-range and long-range characteristics of financial time series, and then applies this method to the five time series of five properties in four stock indices. Combining the two analysis techniques of Rényi entropy and multifractal detrended fluctuation analysis (MFDFA), the 3MPAR method focuses on the curves of Rényi entropy and generalized Hurst exponent of five properties of four stock time series, which allows us to study more universal and subtle fluctuation characteristics of financial time series. By analyzing the curves of the Rényi entropy and the profiles of the logarithm distribution of MFDFA of five properties of four stock indices, the 3MPAR method shows some fluctuation characteristics of the financial time series and the stock markets. Then, it also shows a richer information of the financial time series by comparing the profile of five properties of four stock indices. In this paper, we not only focus on the multifractality of time series but also the fluctuation characteristics of the financial time series and subtle differences in the time series of different properties. We find that financial time series is far more complex than reported in some research works using one property of time series.

A Literature Survey of Early Time Series Classification and Deep Learning

OpenAIRE

Santos, Tiago; Kern, Roman

2017-01-01

This paper provides an overview of current literature on time series classification approaches, in particular of early time series classification. A very common and effective time series classification approach is the 1-Nearest Neighbor classier, with different distance measures such as the Euclidean or dynamic time warping distances. This paper starts by reviewing these baseline methods. More recently, with the gain in popularity in the application of deep neural networks to the eld of...

Signal Processing for Time-Series Functions on a Graph

Science.gov (United States)

2018-02-01

Figures Fig. 1 Time -series function on a fixed graph.............................................2 iv Approved for public release; distribution is...φi〉`2(V)φi (39) 6= f̄ (40) Instead, we simply recover the average of f over time . 13 Approved for public release; distribution is unlimited. This...ARL-TR-8276• FEB 2018 US Army Research Laboratory Signal Processing for Time -Series Functions on a Graph by Humberto Muñoz-Barona, Jean Vettel, and

Non-linear time series extreme events and integer value problems

CERN Document Server

Turkman, Kamil Feridun; Zea Bermudez, Patrícia

2014-01-01

This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time ...

Learning of time series through neuron-to-neuron instruction

Energy Technology Data Exchange (ETDEWEB)

Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

2003-02-07

A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

Learning of time series through neuron-to-neuron instruction

International Nuclear Information System (INIS)

Miyazaki, Y; Kinzel, W; Shinomoto, S

2003-01-01

A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

Quirky patterns in time-series of estimates of recruitment could be artefacts

DEFF Research Database (Denmark)

Dickey-Collas, M.; Hinzen, N.T.; Nash, R.D.M.

2015-01-01

of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena......The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment...... employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model...

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

KAUST Repository

Euán, Carolina

2018-04-12

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. The extent of similarity between a pair of time series is measured using the total variation distance between their estimated spectral densities. At each step of the algorithm, every time two clusters merge, a new spectral density is estimated using the whole information present in both clusters, which is representative of all the series in the new cluster. The method is implemented in an R package HSMClust. We present two applications of the HSM method, one to data coming from wave-height measurements in oceanography and the other to electroencefalogram (EEG) data.

Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series

International Nuclear Information System (INIS)

Albers, D.J.; Hripcsak, George

2012-01-01

Highlights: ► Time-delayed mutual information for irregularly sampled time-series. ► Estimation bias for the time-delayed mutual information calculation. ► Fast, simple, PDF estimator independent, time-delayed mutual information bias estimate. ► Quantification of data-set-size limits of the time-delayed mutual calculation. - Abstract: A method to estimate the time-dependent correlation via an empirical bias estimate of the time-delayed mutual information for a time-series is proposed. In particular, the bias of the time-delayed mutual information is shown to often be equivalent to the mutual information between two distributions of points from the same system separated by infinite time. Thus intuitively, estimation of the bias is reduced to estimation of the mutual information between distributions of data points separated by large time intervals. The proposed bias estimation techniques are shown to work for Lorenz equations data and glucose time series data of three patients from the Columbia University Medical Center database.

Inferring interdependencies from short time series

Indian Academy of Sciences (India)

Abstract. Complex networks provide an invaluable framework for the study of interlinked dynamical systems. In many cases, such networks are constructed from observed time series by first estimating the ...... does not quantify causal relations (unlike IOTA, or .... Africa_map_regions.svg, which is under public domain.

Google and the "Twisted Cyber Spy" Affair: US-Chinese Communication in an Age of Globalization

Science.gov (United States)

Hartnett, Stephen John

2011-01-01

The "twisted cyber spy" affair began in 2010, when Google was attacked by Chinese cyber-warriors charged with stealing Google's intellectual property, planting viruses in its computers, and hacking the accounts of Chinese human rights activists. In the ensuing international embroglio, the US mainstream press, corporate leaders, and White…

Stochastic modeling of hourly rainfall times series in Campania (Italy)

Science.gov (United States)

Giorgio, M.; Greco, R.

2009-04-01

Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil

Application of Statistical, Fuzzy and Perceptron Neural Networks in Drought Forecasting (Case Study: Gonbad-e Kavous Station

Directory of Open Access Journals (Sweden)

S.M. Hosseini-Moghari

2016-10-01

analysis, the optimal precipitation distribution in many cases was not Gamma distribution. The various time-scales of SPI revealed that, at least in 50% of the events, Gamma was not the selected distribution. Throughout the drought forecasting on the basis of SPI time-series with four aforementioned networks, 80% of the data was allocated to the training process whilst the rest of them considered for the test process. The proper parameters of the networks were chosen via trial and error. Moreover, Cross-validation was used to overcome the over-estimation. The results revealed that the long-term SPIs outdid the others. Performance of the networks promoted with increases in time scales of SPI. In other words, the performance criteria improved proportional to the increases in the time-scales. Based on the Table 3, the least and best performance were contributed to SPI1 and SPI24, respectively. In this regard, R2 of MLP for observed and estimated values of SPI vitiated from 0.009 to 0.949. Similar to MLP, correlation of ANFIS, RBF, and GRNN increased from 0.021 to 0.925, 0.263 to 0.953, and 0.210 to 0.955. Comparison of observed and estimated mean values via Z test indicated that null hypothesis of equal mean observed and estimated values was only rejected for SPI1 with α=0.01. Hence, except SPI1 forecasting, the all other scenarios have remained the mean of observed time series which highlighted the robustness of artificial intelligence in drought forecasting. Conclusion: The main objective of the ongoing research was monitoring and forecasting of drought based upon various time scales of SPI. In doing so, the precipitation data of Gonbad Kavous station during the period of 1972-73 to 2006-07 were used. Based on K-S test, the best statistical distribution test for different time scales of SPI evaluation was chosen, and then, the SPI was calculated based on the most fitted distribution. After generating the time-series, MLP, ANFIS, RBF, and GRNN were applied for drought

The digital ASIC for the digital front end electronics of the SPI astrophysics gamma-ray experiment

International Nuclear Information System (INIS)

Lafond, E.; Mur, M.; Schanne, S.

1998-01-01

The SPI spectrometer is one of the gamma-ray astronomy instruments that will be installed on the ESA INTEGRAL satellite, intended to be launched in 2001 by the European Space Agency. The Digital Front-End Electronics sub-system (DFEE) is in charge of the real time data processing of the various measurements produced by the Germanium (Ge) detectors and the Bismuth Germanate (BGO) anti-coincidence shield. The central processing unit of the DFEE is implemented in a digital ASIC circuit, which provides the real time association of the various time signals, acquires the associated energy measurements, and classifies the various types of physics events. The paper gives the system constraints of the DFEE, the architecture of the ASIC circuit, the technology requirements, and the strategy for test and integration. Emphasis is given to the high level language development and simulation, the automatic circuit synthesis approach, and the performance estimation

Using forbidden ordinal patterns to detect determinism in irregularly sampled time series.

Science.gov (United States)

Kulp, C W; Chobot, J M; Niskala, B J; Needhammer, C J

2016-02-01

It is known that when symbolizing a time series into ordinal patterns using the Bandt-Pompe (BP) methodology, there will be ordinal patterns called forbidden patterns that do not occur in a deterministic series. The existence of forbidden patterns can be used to identify deterministic dynamics. In this paper, the ability to use forbidden patterns to detect determinism in irregularly sampled time series is tested on data generated from a continuous model system. The study is done in three parts. First, the effects of sampling time on the number of forbidden patterns are studied on regularly sampled time series. The next two parts focus on two types of irregular-sampling, missing data and timing jitter. It is shown that forbidden patterns can be used to detect determinism in irregularly sampled time series for low degrees of sampling irregularity (as defined in the paper). In addition, comments are made about the appropriateness of using the BP methodology to symbolize irregularly sampled time series.

Forecasting the Reference Evapotranspiration Using Time Series Model

Directory of Open Access Journals (Sweden)

H. Zare Abyaneh

2016-10-01

Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference

Complexity testing techniques for time series data: A comprehensive literature review

International Nuclear Information System (INIS)

Tang, Ling; Lv, Huiling; Yang, Fengmei; Yu, Lean

2015-01-01

Highlights: • A literature review of complexity testing techniques for time series data is provided. • Complexity measurements can generally fall into fractality, methods derived from nonlinear dynamics and entropy. • Different types investigate time series data from different perspectives. • Measures, applications and future studies for each type are presented. - Abstract: Complexity may be one of the most important measurements for analysing time series data; it covers or is at least closely related to different data characteristics within nonlinear system theory. This paper provides a comprehensive literature review examining the complexity testing techniques for time series data. According to different features, the complexity measurements for time series data can be divided into three primary groups, i.e., fractality (mono- or multi-fractality) for self-similarity (or system memorability or long-term persistence), methods derived from nonlinear dynamics (via attractor invariants or diagram descriptions) for attractor properties in phase-space, and entropy (structural or dynamical entropy) for the disorder state of a nonlinear system. These estimations analyse time series dynamics from different perspectives but are closely related to or even dependent on each other at the same time. In particular, a weaker self-similarity, a more complex structure of attractor, and a higher-level disorder state of a system consistently indicate that the observed time series data are at a higher level of complexity. Accordingly, this paper presents a historical tour of the important measures and works for each group, as well as ground-breaking and recent applications and future research directions.

Complex dynamic in ecological time series

Science.gov (United States)

Peter Turchin; Andrew D. Taylor

1992-01-01

Although the possibility of complex dynamical behaviors-limit cycles, quasiperiodic oscillations, and aperiodic chaos-has been recognized theoretically, most ecologists are skeptical of their importance in nature. In this paper we develop a methodology for reconstructing endogenous (or deterministic) dynamics from ecological time series. Our method consists of fitting...

Time Series Modelling using Proc Varmax

DEFF Research Database (Denmark)

Milhøj, Anders

2007-01-01

In this paper it will be demonstrated how various time series problems could be met using Proc Varmax. The procedure is rather new and hence new features like cointegration, testing for Granger causality are included, but it also means that more traditional ARIMA modelling as outlined by Box...

SensL B-Series and C-Series silicon photomultipliers for time-of-flight positron emission tomography

Energy Technology Data Exchange (ETDEWEB)

O' Neill, K., E-mail: koneill@sensl.com; Jackson, C., E-mail: cjackson@sensl.com

2015-07-01

Silicon photomultipliers from SensL are designed for high performance, uniformity and low cost. They demonstrate peak photon detection efficiency of 41% at 420 nm, which is matched to the output spectrum of cerium doped lutetium orthosilicate. Coincidence resolving time of less than 220 ps is demonstrated. New process improvements have lead to the development of C-Series SiPM which reduces the dark noise by over an order of magnitude. In this paper we will show characterization test results which include photon detection efficiency, dark count rate, crosstalk probability, afterpulse probability and coincidence resolving time comparing B-Series to the newest pre-production C-Series. Additionally we will discuss the effect of silicon photomultiplier microcell size on coincidence resolving time allowing the optimal microcell size choice to be made for time of flight positron emission tomography systems.

Kriging Methodology and Its Development in Forecasting Econometric Time Series

Directory of Open Access Journals (Sweden)

Andrej Gajdoš

2017-03-01

Full Text Available One of the approaches for forecasting future values of a time series or unknown spatial data is kriging. The main objective of the paper is to introduce a general scheme of kriging in forecasting econometric time series using a family of linear regression time series models (shortly named as FDSLRM which apply regression not only to a trend but also to a random component of the observed time series. Simultaneously performing a Monte Carlo simulation study with a real electricity consumption dataset in the R computational langure and environment, we investigate the well-known problem of “negative” estimates of variance components when kriging predictions fail. Our following theoretical analysis, including also the modern apparatus of advanced multivariate statistics, gives us the formulation and proof of a general theorem about the explicit form of moments (up to sixth order for a Gaussian time series observation. This result provides a basis for further theoretical and computational research in the kriging methodology development.

Use of Time-Series, ARIMA Designs to Assess Program Efficacy.

Science.gov (United States)

Braden, Jeffery P.; And Others

1990-01-01

Illustrates use of time-series designs for determining efficacy of interventions with fictitious data describing drug-abuse prevention program. Discusses problems and procedures associated with time-series data analysis using Auto Regressive Integrated Moving Averages (ARIMA) models. Example illustrates application of ARIMA analysis for…

An algorithm of Saxena-Easo on fuzzy time series forecasting

Science.gov (United States)

Ramadhani, L. C.; Anggraeni, D.; Kamsyakawuni, A.; Hadi, A. F.

2018-04-01

This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

Evolutionary Algorithms for the Detection of Structural Breaks in Time Series

DEFF Research Database (Denmark)

Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid

2013-01-01

Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behavior of the time series changes. Typically, no solid background knowledge of the time...

On modeling panels of time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans)

2002-01-01

textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a

Unsupervised Symbolization of Signal Time Series for Extraction of the Embedded Information

Directory of Open Access Journals (Sweden)

Yue Li

2017-03-01

Full Text Available This paper formulates an unsupervised algorithm for symbolization of signal time series to capture the embedded dynamic behavior. The key idea is to convert time series of the digital signal into a string of (spatially discrete symbols from which the embedded dynamic information can be extracted in an unsupervised manner (i.e., no requirement for labeling of time series. The main challenges here are: (1 definition of the symbol assignment for the time series; (2 identification of the partitioning segment locations in the signal space of time series; and (3 construction of probabilistic finite-state automata (PFSA from the symbol strings that contain temporal patterns. The reported work addresses these challenges by maximizing the mutual information measures between symbol strings and PFSA states. The proposed symbolization method has been validated by numerical simulation as well as by experimentation in a laboratory environment. Performance of the proposed algorithm has been compared to that of two commonly used algorithms of time series partitioning.

Classification of time-series images using deep convolutional neural networks

Science.gov (United States)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Long Range Dependence Prognostics for Bearing Vibration Intensity Chaotic Time Series

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Qing Li

2016-01-01

Full Text Available According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and the optimal embedding dimension and saturated correlation dimension are calculated via the Grassberger–Procaccia (G-P method, respectively, so that the chaotic characteristics of vibration intensity time series can be jointly determined by the largest Lyapunov exponent and phase plane trajectory of vibration intensity time series, meanwhile, the largest Lyapunov exponent is calculated by the Wolf method and phase plane trajectory is illustrated using Duffing-Holmes Oscillator (DHO. The Hurst exponent and long range dependence prediction method are proposed to verify the typical fractional order features and improve the prediction accuracy of bearing vibration intensity time series, respectively. Experience shows that the vibration intensity time series have chaotic properties and the LRD prediction method is better than the other prediction methods (largest Lyapunov, auto regressive moving average (ARMA and BP neural network (BPNN model in prediction accuracy and prediction performance, which provides a new approach for running tendency predictions for rotating machinery and provide some guidance value to the engineering practice.

Critical values for unit root tests in seasonal time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans); B. Hobijn (Bart)

1997-01-01

textabstractIn this paper, we present tables with critical values for a variety of tests for seasonal and non-seasonal unit roots in seasonal time series. We consider (extensions of) the Hylleberg et al. and Osborn et al. test procedures. These extensions concern time series with increasing seasonal

Classification of time series patterns from complex dynamic systems

Energy Technology Data Exchange (ETDEWEB)

Schryver, J.C.; Rao, N.

1998-07-01

An increasing availability of high-performance computing and data storage media at decreasing cost is making possible the proliferation of large-scale numerical databases and data warehouses. Numeric warehousing enterprises on the order of hundreds of gigabytes to terabytes are a reality in many fields such as finance, retail sales, process systems monitoring, biomedical monitoring, surveillance and transportation. Large-scale databases are becoming more accessible to larger user communities through the internet, web-based applications and database connectivity. Consequently, most researchers now have access to a variety of massive datasets. This trend will probably only continue to grow over the next several years. Unfortunately, the availability of integrated tools to explore, analyze and understand the data warehoused in these archives is lagging far behind the ability to gain access to the same data. In particular, locating and identifying patterns of interest in numerical time series data is an increasingly important problem for which there are few available techniques. Temporal pattern recognition poses many interesting problems in classification, segmentation, prediction, diagnosis and anomaly detection. This research focuses on the problem of classification or characterization of numerical time series data. Highway vehicles and their drivers are examples of complex dynamic systems (CDS) which are being used by transportation agencies for field testing to generate large-scale time series datasets. Tools for effective analysis of numerical time series in databases generated by highway vehicle systems are not yet available, or have not been adapted to the target problem domain. However, analysis tools from similar domains may be adapted to the problem of classification of numerical time series data.

Sensitivity analysis of machine-learning models of hydrologic time series

Science.gov (United States)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Apartheid Spies: The Character, the Reader, and the Censor in André Brink’s A Dry White Season

Directory of Open Access Journals (Sweden)

Giuliana Iannaccaro

2014-05-01

Full Text Available André Brink’s novel A Dry White Season (1979 is strictly connected with the time and place in which it was written. Its dialogue with the real Johannesburg of the late Seventies is manifest: characters and plot are grafted on real events, like the youth riots in Soweto (1976 and the death of Steve Biko in the hands of the Security Police (1977. Brink’s early novels have been commended for their political commitment, but also criticized for being ‘easily’ documentary. Yet, A Dry White Season’s structure is rather complex, and its fabricated nature is shown to the reader through the device of the double internal narrator and the considerable number of ‘documents’ discovered and employed – each telling its own truth. The metaphor of the ‘spying gaze’ proposed here is useful in order to point out the interconnections between story and history, as it shows the way in which a complex and multi-layered narrative structure succeeds in both reflecting and exposing the intricate apartheid system devised to control and ‘discipline’ its own citizens. In the novel, most of the characters intrude into the life of the others and are intruded upon, included peaceful citizens who are obliged to defend themselves from an oppressive and violent police State. At the same time, also the reader is drawn into the dangerous spying game, above all when he/she is introduced into that notorious site of interrogation, torture and detention which was the Johannesburg Police Station in John Vorster Square. The last part of the essay takes into consideration the South African censorship system in force at the time, to show that the activity of surveillance is both intrinsic to the narration and external, related to the conditions of the novel’s composition and appearance on the literary market.

Fractal analysis and nonlinear forecasting of indoor 222Rn time series

International Nuclear Information System (INIS)

Pausch, G.; Bossew, P.; Hofmann, W.; Steger, F.

1998-01-01

Fractal analyses of indoor 222 Rn time series were performed using different chaos theory based measurements such as time delay method, Hurst's rescaled range analysis, capacity (fractal) dimension, and Lyapunov exponent. For all time series we calculated only positive Lyapunov exponents which is a hint to chaos, while the Hurst exponents were well below 0.5, indicating antipersistent behaviour (past trends tend to reverse in the future). These time series were also analyzed with a nonlinear prediction method which allowed an estimation of the embedding dimensions with some restrictions, limiting the prediction to about three relative time steps. (orig.)

Koopman Operator Framework for Time Series Modeling and Analysis

Science.gov (United States)

Surana, Amit

2018-01-01

We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.

Testing for intracycle determinism in pseudoperiodic time series.

Science.gov (United States)

Coelho, Mara C S; Mendes, Eduardo M A M; Aguirre, Luis A

2008-06-01

A determinism test is proposed based on the well-known method of the surrogate data. Assuming predictability to be a signature of determinism, the proposed method checks for intracycle (e.g., short-term) determinism in the pseudoperiodic time series for which standard methods of surrogate analysis do not apply. The approach presented is composed of two steps. First, the data are preprocessed to reduce the effects of seasonal and trend components. Second, standard tests of surrogate analysis can then be used. The determinism test is applied to simulated and experimental pseudoperiodic time series and the results show the applicability of the proposed test.

Time series analysis and its applications with R examples

CERN Document Server

Shumway, Robert H

2017-01-01

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonli...

A KST framework for correlation network construction from time series signals

Science.gov (United States)

Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

2018-04-01

A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

Forecasting daily meteorological time series using ARIMA and regression models

Science.gov (United States)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Analysis of complex time series using refined composite multiscale entropy

International Nuclear Information System (INIS)

Wu, Shuen-De; Wu, Chiu-Wen; Lin, Shiou-Gwo; Lee, Kung-Yen; Peng, Chung-Kang

2014-01-01

Multiscale entropy (MSE) is an effective algorithm for measuring the complexity of a time series that has been applied in many fields successfully. However, MSE may yield an inaccurate estimation of entropy or induce undefined entropy because the coarse-graining procedure reduces the length of a time series considerably at large scales. Composite multiscale entropy (CMSE) was recently proposed to improve the accuracy of MSE, but it does not resolve undefined entropy. Here we propose a refined composite multiscale entropy (RCMSE) to improve CMSE. For short time series analyses, we demonstrate that RCMSE increases the accuracy of entropy estimation and reduces the probability of inducing undefined entropy.

Compounding approach for univariate time series with nonstationary variances

Science.gov (United States)

Schäfer, Rudi; Barkhofen, Sonja; Guhr, Thomas; Stöckmann, Hans-Jürgen; Kuhl, Ulrich

2015-12-01

A defining feature of nonstationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent variances. To model the long-term statistical behavior, we compound the local distribution with the distribution of its parameters. Here, we consider two concrete, but diverse, examples of such nonstationary systems: the turbulent air flow of a fan and a time series of foreign exchange rates. Our main focus is to empirically determine the appropriate parameter distribution for the compounding approach. To this end, we extract the relevant time scales by decomposing the time signals into windows and determine the distribution function of the thus obtained local variances.

Tools for Generating Useful Time-series Data from PhenoCam Images

Science.gov (United States)

Milliman, T. E.; Friedl, M. A.; Frolking, S.; Hufkens, K.; Klosterman, S.; Richardson, A. D.; Toomey, M. P.

2012-12-01

The PhenoCam project (http://phenocam.unh.edu/) is tasked with acquiring, processing, and archiving digital repeat photography to be used for scientific studies of vegetation phenological processes. Over the past 5 years the PhenoCam project has collected over 2 million time series images for a total over 700 GB of image data. Several papers have been published describing derived "vegetation indices" (such as green-chromatic-coordinate or gcc) which can be compared to standard measures such as NDVI or EVI. Imagery from our archive is available for download but converting series of images for a particular camera into useful scientific data, while simple in principle, is complicated by a variety of factors. Cameras are often exposed to harsh weather conditions (high wind, rain, ice, snow pile up), which result in images where the field of view (FOV) is partially obscured or completely blocked for periods of time. The FOV can also change for other reasons (mount failures, tower maintenance, etc.) Some of the relatively inexpensive cameras that are being used can also temporarily lose color balance or exposure controls resulting in loss of imagery. All these factors negatively influence the automated analysis of the image time series making this a non-trivial task. Here we discuss the challenges of processing PhenoCam image time-series for vegetation monitoring and the associated data management tasks. We describe our current processing framework and a simple standardized output format for the resulting time-series data. The time-series data in this format will be generated for specific "regions of interest" (ROI's) for each of the cameras in the PhenoCam network. This standardized output (which will be updated daily) can be considered 'the pulse' of a particular camera and will provide a default phenological dynamic for said camera. The time-series data can also be viewed as a higher level product which can be used to generate "vegetation indices", like gcc, for

Multiple Time Series Ising Model for Financial Market Simulations

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2015-01-01

In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.

Science.gov (United States)

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

FTSPlot: fast time series visualization for large datasets.

Directory of Open Access Journals (Sweden)

Michael Riss

Full Text Available The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of O(n x log(N; the visualization itself can be done with a complexity of O(1 and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with < 20 ms ms. The current 64-bit implementation theoretically supports datasets with up to 2(64 bytes, on the x86_64 architecture currently up to 2(48 bytes are supported, and benchmarks have been conducted with 2(40 bytes/1 TiB or 1.3 x 10(11 double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.

Normalization methods in time series of platelet function assays

Science.gov (United States)

Van Poucke, Sven; Zhang, Zhongheng; Roest, Mark; Vukicevic, Milan; Beran, Maud; Lauwereins, Bart; Zheng, Ming-Hua; Henskens, Yvonne; Lancé, Marcus; Marcus, Abraham

2016-01-01

Abstract Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM). The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques. In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series. Normalization was calculated per assay (test) for all time points and per time point for all tests. Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization. PMID:27428217

Development and application of a modified dynamic time warping algorithm (DTW-S) to analyses of primate brain expression time series.

Science.gov (United States)

Yuan, Yuan; Chen, Yi-Ping Phoebe; Ni, Shengyu; Xu, Augix Guohua; Tang, Lin; Vingron, Martin; Somel, Mehmet; Khaitovich, Philipp

2011-08-18

Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such comparisons. While many powerful computation tools for time series alignment have been developed, they do not provide significance estimates for time shift measurements. Here, we present an extended version of the original DTW algorithm that allows us to determine the significance of time shift estimates in time series alignments, the DTW-Significance (DTW-S) algorithm. The DTW-S combines important properties of the original algorithm and other published time series alignment tools: DTW-S calculates the optimal alignment for each time point of each gene, it uses interpolated time points for time shift estimation, and it does not require alignment of the time-series end points. As a new feature, we implement a simulation procedure based on parameters estimated from real time series data, on a series-by-series basis, allowing us to determine the false positive rate (FPR) and the significance of the estimated time shift values. We assess the performance of our method using simulation data and real expression time series from two published primate brain expression datasets. Our results show that this method can provide accurate and robust time shift estimates for each time point on a gene-by-gene basis. Using these estimates, we are able to uncover novel features of the biological processes underlying human brain development and maturation. The DTW-S provides a convenient tool for calculating accurate and robust time shift estimates at each time point for each gene, based on time series data. The estimates can be used to uncover novel biological features of the system being studied. The DTW-S is freely available as an R package TimeShift at http://www.picb.ac.cn/Comparative/data.html.

Automated Bayesian model development for frequency detection in biological time series

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Oldroyd Giles ED

2011-06-01

Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

Automated Bayesian model development for frequency detection in biological time series.

Science.gov (United States)

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time

Standardized precipitation index zones for Mexico

Energy Technology Data Exchange (ETDEWEB)

Giddings, L.; Soto, M. [Instituto de Ecologia, A.C., Xalapa, Veracruz (Mexico); Rutherford, B.M.; Maarouf, A. [Faculty of Environmental Studies, York University, Toronto, Ontario (Canada)

2005-01-01

Precipitation zone systems exists for Mexico based on seasonality, quantity of precipitation, climates and geographical divisions, but none are convenient for the study of the relation of precipitation with phenomena such as El nino. An empirical set of seven exclusively Mexican and six shared zones was derived from three series of Standardized Precipitation Index (SPI) images, from 1940 through 1989: a whole year series (SPI-12) of 582 monthly images, a six month series (SPI-6) of 50 images for winter months (November through April), and a six month series (SPI-6) of 50 images for summer months (May through October). By examination of principal component and unsupervised classification images, it was found that all three series had similar zones. A set of basic training fields chosen from the principal component images was used to classify all three series. The resulting thirteen zones, presented in this article, were found to be approximately similar, varying principally at zones edges. A set of simple zones defined by just a few vertices can be used for practical operations. In general the SPI zones are homogeneous, with almost no mixture of zones and few outliers of one zone in the area of others. They are compared with a previously published map of climatic regions. Potential applications for SPI zones are discussed. [Spanish] Existen varios sistemas de zonificacion de Mexico basados en la estacionalidad, cantidad de precipitacion, climas y divisiones geograficas, pero ninguno es conveniente para el estudio de la relacion de la precipitacion con fenomenos tales como El Nino. En este trabajo se presenta un conjunto de siete zonas empiricas exclusivamente mexicanas y seis compartidas, derivadas de tres series de imagenes de SPI (Indice Estandarizado de la Precipitacion), desde 1940 a 1989: una serie de 582 imagenes mensuales (SPI-12), una series de 50 imagenes (SPI-6) de meses de invierno (noviembre a abril), y otra de 50 imagenes (SPI-6) de meses de verano

hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

Science.gov (United States)

Fulcher, Ben D; Jones, Nick S

2017-11-22

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Determination of the Integral/SPI instrumental response and his application to the observation of gamma ray lines in the Vela region; Determination de la reponse instrumentale du spectrometre INTEGRAL/SPI et application a l'observation des raies gamma de la region des Voiles

Energy Technology Data Exchange (ETDEWEB)

Attie, D

2005-01-15

The INTEGRAL/SPI spectrometer was designed to observe the sky in the energy band of 20 keV to 8 MeV. The specificity of instrument SPI rests on the excellent spectral resolution (2.3 keV with 1 MeV) of its detecting plan, composed of 19 cooled germanium crystals; covering an effective area of 508 cm{sup 2}. The use of a coded mask, located at 1.7 m above the detection plan ensures to it a resolving power of 2.5 degrees. The aim of this thesis, begun before the INTEGRAL launch, is made up of two parts. The first part relates to the analysis of the spectrometer calibration data. The objective was to measure and check the performances of the telescope, in particular to validate simulations of the INTEGRAL/SPI instrument response. This objective was successfully achieved. This analysis also highlights the presence of a significant instrumental background noise. Whereas, the second part concentrates on the data analysis of the Vela region observations. I have approached two astrophysical topics dealing with: - the search for radioactive decays lines of titanium-44, which is produced by explosive nucleosynthesis, in the supernova remnant of Vela Junior and, - the search of cyclotron resonance scattering features expected towards 25 keV and 52 keV in the accreting pulsar spectrum of the x-ray binary star Vela X-1. Putting forward the hypothesis that the result obtained previously by COMPTEL is correct and considering the no-detection of the titanium-44 lines by SPI, we give a lower limit at 4500 km s{sup -1} for the ejecta velocity from Vela Junior. The analysis on the research of the cyclotron lines have shown that the results are very sensitive to the instrumental background. Thorough studies will be necessary to guarantee an unambiguous detection of these lines. (author)

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

Science.gov (United States)

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Applied time series analysis and innovative computing

CERN Document Server

Ao, Sio-Iong

2010-01-01

This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case studies based on recent research.

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

Science.gov (United States)

Thompson, William Hedley; Fransson, Peter

2016-12-01

Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

Characteristics of the transmission of autoregressive sub-patterns in financial time series

Science.gov (United States)

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

2014-09-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

A Review of Some Aspects of Robust Inference for Time Series.

Science.gov (United States)

1984-09-01

REVIEW OF SOME ASPECTSOF ROBUST INFERNCE FOR TIME SERIES by Ad . Dougla Main TE "iAL REPOW No. 63 Septermber 1984 Department of Statistics University of ...clear. One cannot hope to have a good method for dealing with outliers in time series by using only an instantaneous nonlinear transformation of the data...AI.49 716 A REVIEWd OF SOME ASPECTS OF ROBUST INFERENCE FOR TIME 1/1 SERIES(U) WASHINGTON UNIV SEATTLE DEPT OF STATISTICS R D MARTIN SEP 84 TR-53

Refined composite multiscale weighted-permutation entropy of financial time series

Science.gov (United States)

Zhang, Yongping; Shang, Pengjian

2018-04-01

For quantifying the complexity of nonlinear systems, multiscale weighted-permutation entropy (MWPE) has recently been proposed. MWPE has incorporated amplitude information and been applied to account for the multiple inherent dynamics of time series. However, MWPE may be unreliable, because its estimated values show large fluctuation for slight variation of the data locations, and a significant distinction only for the different length of time series. Therefore, we propose the refined composite multiscale weighted-permutation entropy (RCMWPE). By comparing the RCMWPE results with other methods' results on both synthetic data and financial time series, RCMWPE method shows not only the advantages inherited from MWPE but also lower sensitivity to the data locations, more stable and much less dependent on the length of time series. Moreover, we present and discuss the results of RCMWPE method on the daily price return series from Asian and European stock markets. There are significant differences between Asian markets and European markets, and the entropy values of Hang Seng Index (HSI) are close to but higher than those of European markets. The reliability of the proposed RCMWPE method has been supported by simulations on generated and real data. It could be applied to a variety of fields to quantify the complexity of the systems over multiple scales more accurately.

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

Science.gov (United States)

Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

2000-09-01

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

Synthetic river flow time series generator for dispatch and spot price forecast

International Nuclear Information System (INIS)

Flores, R.A.

2007-01-01

Decision-making in electricity markets is complicated by uncertainties in demand growth, power supplies and fuel prices. In Peru, where the electrical power system is highly dependent on water resources at dams and river flows, hydrological uncertainties play a primary role in planning, price and dispatch forecast. This paper proposed a signal processing method for generating new synthetic river flow time series as a support for planning and spot market price forecasting. River flow time series are natural phenomena representing a continuous-time domain process. As an alternative synthetic representation of the original river flow time series, this proposed signal processing method preserves correlations, basic statistics and seasonality. It takes into account deterministic, periodic and non periodic components such as those due to the El Nino Southern Oscillation phenomenon. The new synthetic time series has many correlations with the original river flow time series, rendering it suitable for possible replacement of the classical method of sorting historical river flow time series. As a dispatch and planning approach to spot pricing, the proposed method offers higher accuracy modeling by decomposing the signal into deterministic, periodic, non periodic and stochastic sub signals. 4 refs., 4 tabs., 13 figs

Cross-sample entropy of foreign exchange time series

Science.gov (United States)

Liu, Li-Zhi; Qian, Xi-Yuan; Lu, Heng-Yao

2010-11-01

The correlation of foreign exchange rates in currency markets is investigated based on the empirical data of DKK/USD, NOK/USD, CAD/USD, JPY/USD, KRW/USD, SGD/USD, THB/USD and TWD/USD for a period from 1995 to 2002. Cross-SampEn (cross-sample entropy) method is used to compare the returns of every two exchange rate time series to assess their degree of asynchrony. The calculation method of confidence interval of SampEn is extended and applied to cross-SampEn. The cross-SampEn and its confidence interval for every two of the exchange rate time series in periods 1995-1998 (before the Asian currency crisis) and 1999-2002 (after the Asian currency crisis) are calculated. The results show that the cross-SampEn of every two of these exchange rates becomes higher after the Asian currency crisis, indicating a higher asynchrony between the exchange rates. Especially for Singapore, Thailand and Taiwan, the cross-SampEn values after the Asian currency crisis are significantly higher than those before the Asian currency crisis. Comparison with the correlation coefficient shows that cross-SampEn is superior to describe the correlation between time series.

Clustering Multivariate Time Series Using Hidden Markov Models

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Shima Ghassempour

2014-03-01

Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

Science.gov (United States)

Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

2017-12-01

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at

Stochastic generation of hourly wind speed time series

International Nuclear Information System (INIS)

Shamshad, A.; Wan Mohd Ali Wan Hussin; Bawadi, M.A.; Mohd Sanusi, S.A.

2006-01-01

In the present study hourly wind speed data of Kuala Terengganu in Peninsular Malaysia are simulated by using transition matrix approach of Markovian process. The wind speed time series is divided into various states based on certain criteria. The next wind speed states are selected based on the previous states. The cumulative probability transition matrix has been formed in which each row ends with 1. Using the uniform random numbers between 0 and 1, a series of future states is generated. These states have been converted to the corresponding wind speed values using another uniform random number generator. The accuracy of the model has been determined by comparing the statistical characteristics such as average, standard deviation, root mean square error, probability density function and autocorrelation function of the generated data to those of the original data. The generated wind speed time series data is capable to preserve the wind speed characteristics of the observed data