Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

DEFF Research Database (Denmark)

Sørup, Hjalte Jomo Danielsen; Georgiadis, Stylianos; Gregersen, Ida Bülow

2017-01-01

Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems......, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings...... in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill...

City spaces - tourist places : urban tourism precincts

NARCIS (Netherlands)

Grigolon, A.B.

2011-01-01

Urban tourism precincts can be defined as ‘an area in which various attractions such as bars, restaurants, places of entertainment or education, accommodation, amenities and other facilities that are clustered in freely accessible public spaces. Tourism precincts by their nature enhance certain

Defining Place Attachment in Asian Urban Places through Opportunities for Social Interactions

Directory of Open Access Journals (Sweden)

Norsidah Ujang

2016-01-01

Full Text Available Despite the high intensity of urban dwellers and the growing needs for socialization outdoor, the opportunity for interaction is limited due to the lack of public open spaces. This paper discusses the use of public open spaces in a city of Kuala Lumpur and how it shapes users’ attachment. Field observations and face to face interviews were conducted to examine the opportunities for social activities and pattern of users’ engagement. The findings indicate the incapability of the places to provide multifunctional spaces for diverse interactions while the social attachment to the places is strongly defined by interaction with familiar people in place.

Acute and Subacute Effects of Urban Air Pollution on Cardiopulmonary Emergencies and Mortality: Time Series Studies in Austrian Cities

Directory of Open Access Journals (Sweden)

Daniel Rabczenko

2013-10-01

Full Text Available Daily pollution data (collected in Graz over 16 years and in the Linz over 18 years were used for time series studies (GAM and case-crossover on the relationship with daily mortality (overall and specific causes of death. Diagnoses of patients who had been transported to hospitals in Linz were also available on a daily basis from eight years for time series analyses of cardiopulmonary emergencies. Increases in air pollutant levels over several days were followed by increases in mortality and the observed effects increased with the length of the exposure window considered, up to a maximum of 15 days. These mortality changes in Graz and Linz showed similar patterns like the ones found before in Vienna. A significant association of mortality could be demonstrated with NO2, PM2.5 and PM10 even in summer, when concentrations are lower and mainly related to motor traffic. Cardiorespiratory ambulance transports increased with NO2/PM2.5/PM10 by 2.0/6.1/1.7% per 10 µg/m³ on the same day. Monitoring of NO2 (related to motor traffic and fine particulates at urban background stations predicts acute effects on cardiopulmonary emergencies and extended effects on cardiopulmonary mortality. Both components of urban air pollution are indicators of acute cardiopulmonary health risks, which need to be monitored and reduced, even below current standards.

Place Mapping – transect walks in Arctic urban landscapes

Directory of Open Access Journals (Sweden)

Peter Hemmersam

2016-11-01

Full Text Available This article investigates how experimental forms of urban mapping can reveal the particularity of places in non-standard urban situations with the intention of moving beyond the reductivism of still-dominant modernist modes of mapping and associated forms of planning. In order to do so, it reports on the emergence of a methodology involving transect walks, with the purpose of mapping the peculiarities of cultural landscapes. The study is located in cities and communities in the Arctic that are undergoing rapid transformation and are in urgent need of new conceptual approaches capable of enabling future thinking and strategic action. The article specifically asks how such a methodology works to includes the ephemeral and emergent, but also digital, dimensions of urban landscapes, and results in a complex reflexive method of critically reading and writing, of moving and locating, of seeing and picturing place mapping.

"Letter-Space": Typographic Translations of Urban Place

Science.gov (United States)

Naismith, Jacqueline; O'Sullivan, Annette

2011-01-01

This article discusses a Bachelor of Design honours year typography project in the medium of letterpress. The "Letter-space" project positioned letterpress as a textual, spatial and structural visual language, through which the experiences and meanings of a local urban place were translated, mapped and given form through typographic design. We…

Wellbeing in Urban Greenery: The Role of Naturalness and Place Identity.

Science.gov (United States)

Knez, Igor; Ode Sang, Åsa; Gunnarsson, Bengt; Hedblom, Marcus

2018-01-01

The aim was to investigate effects of urban greenery (high vs. low naturalness) on place identity and wellbeing, and the links between place identity and wellbeing. It was shown that participants (Gothenburg, Sweden, N = 1347) estimated a stronger attachment/closeness/belonging (emotional component of place-identity), and more remembrance and thinking about and mental travel (cognitive component of place-identity) in relation to high vs. low perceived naturalness. High naturalness was also reported to generate higher wellbeing in participants than low naturalness. Furthermore, place identity was shown to predict participants' wellbeing in urban greenery, accounting for 35% of variance explained by the regression. However, there was a stronger relationship between the emotional vs. the cognitive component of place identity and wellbeing. Finally, a significant role of place identity in mediating the naturalness-wellbeing relationship was shown, indicating that the naturalness-wellbeing connection can be partly accounted for by the psychological mechanisms of people-place bonding.

A map of urban tales. Laboratory Q, a place for urban creativity

OpenAIRE

Antonio Alanis Arroyo; María F. Carrascal Pérez; Plácido González Martínez

2015-01-01

Laboratory Q, a place for urban creativity is a research platform set up to study the contemporary city. Contained in a participatory virtual map are processes, spaces, and creative actions that took place in Seville between 1996 and 2012, the last part of which was defined by the economic crisis. This article is structured like a manifesto and presents the structural foundations that hold up the project. Each declaration works autonomously in relation to everything, describes the ideal strat...

Place visitation, place avoidance, and attitudinal ambivalence: new concepts for place research in urban recreation settings

Science.gov (United States)

David B. Klenosky; Christine A. Vogt; Herbert W. Schroeder; Cherie LeBlanc Fisher

2010-01-01

This paper draws on recent developments in research on consumer behavior and attitudes to better understand the range of behaviors and attitudes inherent in a diverse urban area. Using a mail survey of Chicago-area residents, we collected data (1) to examine residents' past visitation behavior and recommendations of places to visit and to avoid for a range of...

Estimating the Impact of Urban Expansion on Land Subsidence Using Time Series of DMSP Night-Time Light Satellite Imagery

Science.gov (United States)

Jiao, S.; Yu, J.; Wang, Y.; Zhu, L.; Zhou, Q.

2018-04-01

In recent decades, urbanization has resulted a massive increase in the amount of infrastructure especially large buildings in large cities worldwide. There has been a noticeable expansion of entire cities both horizontally and vertically. One of the common consequences of urban expansion is the increase of ground loads, which may trigger land subsidence and can be a potential threat of public safety. Monitoring trends of urban expansion and land subsidence using remote sensing technology is needed to ensure safety along with urban planning and development. The Defense Meteorological Satellite Program Operational Line scan System (DMSP/OLS) Night-Time Light (NTL) images have been used to study urbanization at a regional scale, proving the capability of recognizing urban expansion patterns. In the current study, a normalized illuminated urban area dome volume (IUADV) based on inter-calibrated DMSP/OLS NTL images is shown as a practical approach for estimating urban expansion of Beijing at a single period in time and over subsequent years. To estimate the impact of urban expansion on land subsidence, IUADV was correlated with land subsidence rates obtained using the Stanford Method for Persistent Scatterers (StaMPS) approach within the Persistent Scatterers InSAR (PSInSAR) methodology. Moderate correlations are observed between the urban expansion based on the DMSP/OLS NTL images and land subsidence. The correlation coefficients between the urban expansion of each year and land subsidence tends to gradually decrease over time (Coefficient of determination R = 0.80 - 0.64 from year 2005 to year 2010), while the urban expansion of two sequential years exhibit an opposite trend (R = 0.29 - 0.57 from year 2005 to year 2010) except for the two sequential years between 2007 and 2008 (R = 0.14).

The changing nature of urban public places in Dhaka City

Directory of Open Access Journals (Sweden)

Mashrur Rahman Mishu

2014-12-01

Full Text Available Throughout the history, public places have been asserted as one of the key components of urban life for their physical, social, political, symbolic and environmental roles. However, the nature and quality of public places in recent years have raised the question how far these places remain ‘public’ in true sense. The study systematically explores how the public places of Dhaka have transformed throughout the history in different time periods. It attempts to assess the ‘publicness’ of the existing public places focusing on the changing nature of these places and the tensions arise from different perspectives. The research is descriptive and employs a case study approach. Osmany Uddan, a park situated in the prime location in the city center and the Hatirjheel, a recently developed lakeside area, have been considered as two cases. The findings from the case studies reveal that although these places are public considering the ownership, their quality and characteristics as public place are diminishing day by day. Limited physical and social accessibility have narrowed the group of users who can use the public place for a variety of purposes. Another major phenomenon which can be attributed to the changing nature of public place is the growing private interest. In this backdrop, it needs planning and design considerations to make public place more inclusive to diverse groups of people as such these places can perform multiple functions in balance.

Toward time-based design: Creating an applied time evaluation checklist for urban design research

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Amir Shakibamanesh

2017-09-01

Full Text Available The perception of a 3D space, in which movement takes place, is subjectively based on experience. The pedestrians’ perception of subjective duration is one of the related issues that receive little attention in urban design literature. Pedestrians often misperceive the required time to pass a certain distance. A wide range of factors affects one׳s perception of time in urban environments. These factors include individual factors (e.g., gender, age, and psychological state, social and cultural contexts, purpose and motivation for being in the space, and knowledge of the given area. This study aims to create an applied checklist that can be used by urban designers in analyzing the effects of individual experience on subjective duration. This checklist will enable urban designers to perform a phenomenological assessment of time perception and compare this perception in different urban spaces, thereby improving pedestrians’ experiences of time through a purposeful design. A combination of exploratory and descriptive analytical research is used as methodology due to the complexity of time perception.

Role of environmental rights in the urban design of public places

Directory of Open Access Journals (Sweden)

A.R. Sadeghi

2016-07-01

Full Text Available In the current period, followed by the industrial revolution, the damaging effects of the one-dimensional attitude towards the environment caused by human have had countless hazards. To cope with these risks, the respect and protection of environmental values has attracted today's urban human attention once again and the issues about the human right to a decent, safe and healthy environment which is called briefly" environmental rights ", have widely been discussed. In fact, this research is formed on the basis of the principle that the right to a healthy environment, must be respected in the design of public spaces and the legal aspects of this principle must be considered in dealing with these spaces, so one of the necessary contexts to the conversion of today’s public spaces to valuable urban places would be provided. Therefore, in this study the human right to a healthy, safe and decent environment and the related concepts has been reviewed and the role of the environment in the process of transforming urban spaces to urban places has been discussed. This study also emphasizes on the role of the noise pollution of the urban public spaces as one of the threatening factors of the right to the environment, in the inefficiency and disorder in the process of the conversion of these spaces to public places and while reviewing the laws to reduce such pollution in urban public spaces, it stresses the necessity of considering these rules in designing the urban public spaces. This study uses descriptive and analytic research methodology and investigation techniques of literature review by using library studies.

Urban Times

DEFF Research Database (Denmark)

Nielsen, Morten

2017-01-01

This is a proposed special issue with six thematic articles by different contributors on 'urban times' edited by me.......This is a proposed special issue with six thematic articles by different contributors on 'urban times' edited by me....

Child- and elder-friendly urban public places in Fatahillah Square Historical District

Science.gov (United States)

Srinaga, F.; LKatoppo, M.; Hidayat, J.

2018-03-01

Fatahillah square as an important historical urban square in Jakarta has problems in eye level area integrative processing. Visitors cannot enjoy their time while in the square regarding their visuals, feelings, space, and bodies comfort. These also lead to other problems in which the square is lack of friendly and convenient places for children, the elderly and also the disabled, especially people with limited moving space. The research will attempt in proposing design inception for the Fatahillah Square that is using inclusive user-centered design approach, while in the same time incorporate theoretical studies of children and elderly-design considerations. The first stage of this research was building inclusive design parameter; begin with a context-led research which assesses the quality of Fatahillah square through three basic components of urban space: hardware, software and orgware. The second stage of this research is to propose inclusive design inception for the Fatahillah square.

Does the Visibility of Greenery Increase Perceived Safety in Urban Areas? Evidence from the Place Pulse 1.0 Dataset

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Xiaojiang Li

2015-07-01

Full Text Available Urban green space provides a series of esthetic, environmental and psychological benefits to urban residents. However, the relationship between the visibility of green vegetation and perceived safety is still in debate. This research investigated whether green vegetation could help to increase the perceived safety based on a crowdsourced dataset: the Place Pulse 1.0 dataset. Place Pulse 1.0 dataset, which was generated from a large number of votes by online participants, includes geo-tagged Google Street View images and the corresponding perceived safety score for each image. In this study, we conducted statistical analyses to analyze the relationship between perceived safety and green vegetation characteristics, which were extracted from Google Street View images. Results show that the visibility of green vegetation plays an important role in increasing perceived safety in urban areas. For different land use types, the relationship between vegetation structures and perceived safety varies. In residential, urban public/institutional, commercial and open land areas, the visibility of vegetation higher than 2.5 m has significant positive correlations with perceived safety, but there exists no significant correlation between perceived safety and the percentage of green vegetation in transportation and industrial areas. The visibility of vegetation below 2.5 m has no significant relationship with the perceived safety in almost all land use types, except for multifamily residential land and urban public/institutional land. In general, this study provided insight for the relationship between green vegetation characteristics and the perception of environment, as well as valuable reference data for developing urban greening programs.

Time Series in Education: The Analysis of Daily Attendance in Two High Schools

Science.gov (United States)

Koopmans, Matthijs

2011-01-01

This presentation discusses the use of a time series approach to the analysis of daily attendance in two urban high schools over the course of one school year (2009-10). After establishing that the series for both schools were stationary, they were examined for moving average processes, autoregression, seasonal dependencies (weekly cycles),…

Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models

Science.gov (United States)

Lawson, Anneka Ruth; Ghosh, Bidisha; Broderick, Brian

2011-09-01

Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available.

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.

A Fast Multi-layer Subnetwork Connection Method for Time Series InSAR Technique

Directory of Open Access Journals (Sweden)

WU Hong'an

2016-10-01

Full Text Available Nowadays, times series interferometric synthetic aperture radar (InSAR technique has been widely used in ground deformation monitoring, especially in urban areas where lots of stable point targets can be detected. However, in standard time series InSAR technique, affected by atmospheric correlation distance and the threshold of linear model coherence, the Delaunay triangulation for connecting point targets can be easily separated into many discontinuous subnetworks. Thus it is difficult to retrieve ground deformation in non-urban areas. In order to monitor ground deformation in large areas efficiently, a novel multi-layer subnetwork connection (MLSC method is proposed for connecting all subnetworks. The advantage of the method is that it can quickly reduce the number of subnetworks with valid edges layer-by-layer. This method is compared with the existing complex network connecting mehod. The experimental results demonstrate that the data processing time of the proposed method is only 32.56% of the latter one.

Seven place-conscious methods to stimulate situational interest in science teaching in urban environments

DEFF Research Database (Denmark)

Bølling, Mads; Hartmeyer, Rikke; Bentsen, Peter

2018-01-01

. The data consisted of transcribed interviews with 4 experienced teachers and 11 pupils. The interviews were elicited by films showing group work in science teaching in urban environments: a parking lot, a green public park and a zoo. We conducted individual interviews with science teachers, while......In this study, we explored how teachers can take advantage of a ‘place’ in urban environments outside the school and thereby stimulate pupils’ situational interest in science teaching. Drawing on the Sophos research method, we conducted a single case study including film-elicited interviews...... places; (3) alignment between the environment and task; (4) integrating minimal cultivated places; (5) providing a science perspective on everyday places; (6) disseminating historical or cultural knowledge of places; and (7) surprises. Starting from a discussion drawing on studies that explored triggers...

Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

Directory of Open Access Journals (Sweden)

H. Sadeghi

2016-02-01

Full Text Available Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety of water demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand in better planning of water resources in arid and semiarid regions are faced with water restrictions. Materials and Methods: One of the most important factors affecting the changing technological advances in production and demand functions, we must pay special attention to the layout pattern. Technology development is concerned not only technically, but also other aspects such as personal, non-economic factors (population, geographical and social factors can be analyzed. Model examined in this study, a regression model is composed of a series of structural components over time allows changed invisible accidentally. Explanatory variables technology (both crystalline and amorphous in a model according to which the material is said to be better, but because of the lack of measured variables over time can not be entered in the template. Model examined in this study, a regression model is composed of a series of structural component invisible accidentally changed over time allows. In this study, structural time series (STSM and ARMA time series models have been used to model and estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models have been compared to each other. The desired data in this research include water consumption in Isfahan, water price and the monthly pay

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

Estimating changes in urban land and urban population using refined areal interpolation techniques

Science.gov (United States)

Zoraghein, Hamidreza; Leyk, Stefan

2018-05-01

The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Target Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combinations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead, it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.

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.

Unhealthy places: the ecology of risk in the urban landscape

National Research Council Canada - National Science Library

Fitzpatrick, Kevin M; LaGory, Mark

2000-01-01

... or retrieval system without permission in writing from the publishers. Library of Congress Cataloging-in-Publication Data Fitzpatrick, Kevin M. Unhealthy places: the ecology of risk in the urban landscape/ Kevin M.Fitzpatrick, Mark E.LaGory p. cm. Includes bibliographical references and index. ISBN 0-415-92371-9 (hb).-ISBN 0-415-92372-7 (pb) 1. In...

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

Sowing resilience and contestation in times of crises: the case of urban gardening movements in Barcelona

OpenAIRE

Camps-Calvet, Marta; Langemeyer, Johannes; Calvet-Mir, Laura; Gomez-Baggethun, Erik; March, Hug

2015-01-01

Urban gardens have been observed to multiply in response to crises. However, the meaning and motivations behind the emergence of gardening movements varies greatly over space and time. In this paper we argue that bottom up urban gardening initiatives taking place in Southern European countries in form of land occupation and communalization represent forms of resistance that enhance social cohesion and collective action in times of need. Specifically, this research examines the role of urban g...

The Characters and Meaning of Third Place in Historical Urban Space of Iran

Directory of Open Access Journals (Sweden)

Ahad Nejad Ebrahimi

2017-12-01

Full Text Available Third place is the interface between work and life and due to the direct connection with urban development. It is a valuable space for attending the community in which one attends voluntarily, informally, and regularly or irregularly. There are such places in urban areas of Iran where people are able to attend in order to do social and religious activities and it seems like that the architectural nature of such spaces has some similarities and differences with the definition of third place. The research question is, “what are the features of third place in pre-modern cities of Iran and are the features in accordance with the definition of third place?”. This is a developmental research conducted via the interpretive-historical method. The findings indicate that third place is commonplace in Iranian Cities and some architecture types like public, religious and residential spaces have fundamental similarities with the definitions of third place In Iranian Historical cities, but there are also some differences due to culture, religion, and climate in each region. Third places have widely exited in most applications and religious relations, rituals, and beliefs which demonstrate that brotherhood and communion have significantly influenced the formation of this place. The main issue in this regard is the firm presence of religion and strong ethnocultural ties which have affected the constituents of third place as components like the constant presence of water, creation of special, simple, and defined spaces, respect to adults and providing special furniture for them, and focusing on geometry and aesthetic proportions.

The prospects for urban densification: a place-based study

International Nuclear Information System (INIS)

Schmidt-Thomé, Kaisa; Haybatollahi, Mohammad; Kyttä, Marketta; Korpi, Jari

2013-01-01

Study of the environmental outcomes of urban densification is a highly context-dependent task. Our study shows that collecting and processing place-based survey data by means of the softGIS method is clearly helpful here. With the map-based internet questionnaire each response remains connected to both the physical environment and the everyday life of the respondent. In our study of the Kuninkaankolmio area (located in the Helsinki metropolitan region) the survey data were combined with urban density variables calculated from register-based data on the existing built environment. The regression analysis indicated that the participants in the survey preferred the same density factors for their future residence as they enjoyed in their current neighbourhood. In the second analysis we related the densities of planned infill developments with the interest respondents had shown in these projects. The results show that new and even quite dense infill developments have been found to be rather attractive, with them often being viewed as interesting supplements to the current urban texture. These findings contribute to the ongoing scientific discussion on the feasibility of densification measures and encourage the Kuninkaankolmio planners to proceed, albeit carefully, with the planned infill developments. (letter)

The long-term prospects of citizens managing urban green space: From place making to place-keeping? : Special feature:TURFGRASS

NARCIS (Netherlands)

Mattijssen, T.J.M.; van der Jagt, A.P.N.; Buijs, A.E.; Elands, B.H.M.; Erlwein, S.; Lafortezza, R.

2017-01-01

Abstract This paper discusses the long-term management or ‘place-keeping’ of urban green space by citizens and highlights enabling and constraining factors that play a crucial role in this continuity. While authorities have historically been in charge of managing public green spaces, there is an

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...

Spectral Estimation of UV-Vis Absorbance Time Series for Water Quality Monitoring

Directory of Open Access Journals (Sweden)

Leonardo Plazas-Nossa

2017-05-01

Full Text Available Context: Signals recorded as multivariate time series by UV-Vis absorbance captors installed in urban sewer systems, can be non-stationary, yielding complications in the analysis of water quality monitoring. This work proposes to perform spectral estimation using the Box-Cox transformation and differentiation in order to obtain stationary multivariate time series in a wide sense. Additionally, Principal Component Analysis (PCA is applied to reduce their dimensionality. Method: Three different UV-Vis absorbance time series for different Colombian locations were studied: (i El-Salitre Wastewater Treatment Plant (WWTP in Bogotá; (ii Gibraltar Pumping Station (GPS in Bogotá; and (iii San-Fernando WWTP in Itagüí. Each UV-Vis absorbance time series had equal sample number (5705. The esti-mation of the spectral power density is obtained using the average of modified periodograms with rectangular window and an overlap of 50%, with the 20 most important harmonics from the Discrete Fourier Transform (DFT and Inverse Fast Fourier Transform (IFFT. Results: Absorbance time series dimensionality reduction using PCA, resulted in 6, 8 and 7 principal components for each study site respectively, altogether explaining more than 97% of their variability. Values of differences below 30% for the UV range were obtained for the three study sites, while for the visible range the maximum differences obtained were: (i 35% for El-Salitre WWTP; (ii 61% for GPS; and (iii 75% for San-Fernando WWTP. Conclusions: The Box-Cox transformation and the differentiation process applied to the UV-Vis absorbance time series for the study sites (El-Salitre, GPS and San-Fernando, allowed to reduce variance and to eliminate ten-dency of the time series. A pre-processing of UV-Vis absorbance time series is recommended to detect and remove outliers and then apply the proposed process for spectral estimation. Language: Spanish.

Designing Urban Bikescapes

DEFF Research Database (Denmark)

Marling, Gitte

2014-01-01

This article presents analyses of the ‘Nørrebro Bike Route’ as an ‘urban bikescape’ consisting of a mixture of lanes and coupled urban places and small parks. It is a place to sit, to play and to relax, but at the same time it also a place for mobility. It is a social-technical assemblage (Urry 2...... (scale, rhythm, content) & aesthetics developed? (Thies- Evensen 1992, Venturi 1972, Rasmussen 2003, Thrift 2004, Merleau-Ponty 2009, Pallismaa 2005, Pink 2009)? Finally the article addresses the travelling ideas of ‘new urban bikescapes’ and Nordic urban space design....

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.

Mapping Impervious Surface Expansion using Medium-resolution Satellite Image Time Series: A Case Study in the Yangtze River Delta, China

Science.gov (United States)

Gao, Feng; DeColstoun, Eric Brown; Ma, Ronghua; Weng, Qihao; Masek, Jeffrey G.; Chen, Jin; Pan, Yaozhong; Song, Conghe

2012-01-01

Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China-Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived

Environment and Urban Tourism: AN Emergent System in Rhetorical Place Identity Definitions

Science.gov (United States)

Mura, Marina

Within the systemic framework of Environmental Psychology (Bechtel and Churchman, 2002) and following Urry (2002) and Pearce's approaches (2005), the aim of this research is to investigate within the context of urban tourism which world views emerge from a Discourse Analysis (Edwards, Potter, 1993). of the speech of native and non-native Sardinian residents. It addresses the issue of how social-physical diversity might be preserved (the problem of tourism sustainability, Di Castri, Balaji, 2002). In this regard, forty in-depth narrative interviews of inhabitants with short- and long-term residential experience in Cagliari (Italy) were conducted and examined (Discourse Analysis). It was found that the native and non-native's rhetorical devices expressed similar representations of urban places, but in diverse relationship to social and place identity. Their environmental transitions were based on the tourist gaze, or the functional view and heritage pride. This displays some basic central dimensions of sustainable tourism.

Understanding Relationships between Health, Ethnicity, Place and the Role of Urban Green Space in Deprived Urban Communities

Directory of Open Access Journals (Sweden)

Jenny Roe

2016-07-01

Full Text Available Very little is known about how differences in use and perceptions of urban green space impact on the general health of black and minority ethnic (BME groups. BME groups in the UK suffer from poorer health and a wide range of environmental inequalities that include poorer access to urban green space and poorer quality of green space provision. This study used a household questionnaire (n = 523 to explore the relationship between general health and a range of individual, social and physical environmental predictors in deprived white British and BME groups living in ethnically diverse cities in England. Results from Chi-Squared Automatic Interaction Detection (CHAID segmentation analyses identified three distinct general health segments in our sample ranging from “very good” health (people of Indian origin, to ”good” health (white British, and ”poor” health (people of African-Caribbean, Bangladeshi, Pakistani origin and other BME groups, labelled ”Mixed BME” in the analyses. Correlated Component Regression analyses explored predictors of general health for each group. Common predictors of general health across all groups were age, disability, and levels of physical activity. However, social and environmental predictors of general health-including use and perceptions of urban green space-varied among the three groups. For white British people, social characteristics of place (i.e., place belonging, levels of neighbourhood trust, loneliness ranked most highly as predictors of general health, whilst the quality of, access to and the use of urban green space was a significant predictor of general health for the poorest health group only, i.e., in ”Mixed BME”. Results are discussed from the perspective of differences in use and perceptions of urban green space amongst ethnic groups. We conclude that health and recreation policy in the UK needs to give greater attention to the provision of local green space amongst poor BME

Understanding Relationships between Health, Ethnicity, Place and the Role of Urban Green Space in Deprived Urban Communities

Science.gov (United States)

Roe, Jenny; Aspinall, Peter A.; Ward Thompson, Catharine

2016-01-01

Very little is known about how differences in use and perceptions of urban green space impact on the general health of black and minority ethnic (BME) groups. BME groups in the UK suffer from poorer health and a wide range of environmental inequalities that include poorer access to urban green space and poorer quality of green space provision. This study used a household questionnaire (n = 523) to explore the relationship between general health and a range of individual, social and physical environmental predictors in deprived white British and BME groups living in ethnically diverse cities in England. Results from Chi-Squared Automatic Interaction Detection (CHAID) segmentation analyses identified three distinct general health segments in our sample ranging from “very good” health (people of Indian origin), to ”good” health (white British), and ”poor” health (people of African-Caribbean, Bangladeshi, Pakistani origin and other BME groups), labelled ”Mixed BME” in the analyses. Correlated Component Regression analyses explored predictors of general health for each group. Common predictors of general health across all groups were age, disability, and levels of physical activity. However, social and environmental predictors of general health-including use and perceptions of urban green space-varied among the three groups. For white British people, social characteristics of place (i.e., place belonging, levels of neighbourhood trust, loneliness) ranked most highly as predictors of general health, whilst the quality of, access to and the use of urban green space was a significant predictor of general health for the poorest health group only, i.e., in ”Mixed BME”. Results are discussed from the perspective of differences in use and perceptions of urban green space amongst ethnic groups. We conclude that health and recreation policy in the UK needs to give greater attention to the provision of local green space amongst poor BME communities since this

Understanding Relationships between Health, Ethnicity, Place and the Role of Urban Green Space in Deprived Urban Communities.

Science.gov (United States)

Roe, Jenny; Aspinall, Peter A; Ward Thompson, Catharine

2016-07-05

Very little is known about how differences in use and perceptions of urban green space impact on the general health of black and minority ethnic (BME) groups. BME groups in the UK suffer from poorer health and a wide range of environmental inequalities that include poorer access to urban green space and poorer quality of green space provision. This study used a household questionnaire (n = 523) to explore the relationship between general health and a range of individual, social and physical environmental predictors in deprived white British and BME groups living in ethnically diverse cities in England. Results from Chi-Squared Automatic Interaction Detection (CHAID) segmentation analyses identified three distinct general health segments in our sample ranging from "very good" health (people of Indian origin), to "good" health (white British), and "poor" health (people of African-Caribbean, Bangladeshi, Pakistani origin and other BME groups), labelled "Mixed BME" in the analyses. Correlated Component Regression analyses explored predictors of general health for each group. Common predictors of general health across all groups were age, disability, and levels of physical activity. However, social and environmental predictors of general health-including use and perceptions of urban green space-varied among the three groups. For white British people, social characteristics of place (i.e., place belonging, levels of neighbourhood trust, loneliness) ranked most highly as predictors of general health, whilst the quality of, access to and the use of urban green space was a significant predictor of general health for the poorest health group only, i.e., in "Mixed BME". Results are discussed from the perspective of differences in use and perceptions of urban green space amongst ethnic groups. We conclude that health and recreation policy in the UK needs to give greater attention to the provision of local green space amongst poor BME communities since this can play an

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

Does Place Attachment Predict Wildfire Mitigation and Preparedness? A Comparison of Wildland-Urban Interface and Rural Communities.

Science.gov (United States)

Anton, Charis E; Lawrence, Carmen

2016-01-01

Wildfires are a common occurrence in many countries and are predicted to increase as we experience the effects of climate change. As more people are expected to be affected by fires, it is important to increase people's wildfire mitigation and preparation. Place attachment has been theorized to be related to mitigation and preparation. The present study examined place attachment and wildfire mitigation and preparation in two Australian samples, one rural and one on the wildland-urban interface. The study consisted of 300 participants who responded to questionnaires about their place attachment to their homes and local areas, as well as describing their socio-demographic characteristics and wildfire mitigation and preparedness. Hierarchical regression showed that place attachment to homes predicted wildfire mitigation and preparedness in the rural sample but not in the wildland-urban interface sample. The results suggest that place attachment is a motivator for mitigation and preparation only for people living rurally. Reminding rural residents of their attachment to home at the beginning of wildfire season may result in greater mitigation and preparedness. Further research focusing on why attachment does not predict mitigation and preparedness in the wildland-urban interface is needed.

Time series trends of the safety effects of pavement resurfacing.

Science.gov (United States)

Park, Juneyoung; Abdel-Aty, Mohamed; Wang, Jung-Han

2017-04-01

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

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 ...

Probabilistic modelling of overflow, surcharge and flooding in urban drainage using the first-order reliability method and parameterization of local rain series

DEFF Research Database (Denmark)

Thorndahl, Søren; Willems, Patrick

2007-01-01

Failure of urban drainage systems may occur due to surcharge or flooding at specific manholes in the system, or due to overflows from combined sewer systems to receiving waters. To quantify the probability or return period of failure, standard approaches make use of the simulation of design storms...... or long historical rainfall series in a hydrodynamic model of the urban drainage system. In this paper, an alternative probabilistic method is investigated: the First Order Reliability Method (FORM). To apply this method, a long rainfall time series was divided in rain storms (rain events), and each rain...

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…

Let Us Look After the Veins of the Earth: The Sonification of Time-series Field Data by Analog Methods

Science.gov (United States)

Rivera, V. A.; Amaya, L. F.

2017-12-01

In 2016, graduate students from Northwestern University's Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) initiated the Science Sonification and Composition Project, which pairs scientists with student composers to create original music inspired by and utilizing the products of scientific research. In 2017, these pieces were performed at Northwestern for a mixed audience of scientists, musicians, and community members. Sonification of data, or the representation of data as sound, is an increasingly popular method of examining data in the geosciences, especially in astrophysics, where sonification of gravitational waves has recently made major news. Numerical time-series data are often excellent candidates for sonification, as the data can be modified by simple algorithmic means to convert numerical values which represent physical measurements to numerical values representing musical "variables" like volume, pitch, or timbre. Our collaboration, a result of the CIERA initiative, explores methods of sonification that do not involve a simple conversion of data to sound, instead attempting to create sound from data by analog methods. The piece uses both time-series groundwater elevation data and physical soil samples from the locations where the water table measurements were collected. The field site from which both data and samples were collected is Gensburg Markham Prairie, an urban nature preserve on Chicago's south side which hosts a long-term study on the collateral benefits of urban greenspace for stormwater management and storage. Our aim was to combine physical, living elements with technology to mirror the research, where we examine flows and cycles in nature by "taking the pulse" of the landscape using sensing networks. Soil samples were placed in metal vessels outfitted with contact microphones and manipulated by hand and with water, using time-series data as a guide, much like sheet music. This was repeated for samples and

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...

Characterizing the Spatio-Temporal Pattern of Land Surface Temperature through Time Series Clustering: Based on the Latent Pattern and Morphology

Directory of Open Access Journals (Sweden)

Huimin Liu

2018-04-01

Full Text Available Land Surface Temperature (LST is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS satellite image products. The Latent pattern of LST (LLST generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP and the Multi-Scale Shape Index (MSSI which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k -means and the Back-Propagation neural networks (BP-Net. The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional k -means with Euclidean Distance (ED, shape-based k -means with the constrained Dynamic Time Warping ( c DTW distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA centroid computation method ( k - c DBA and k -shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k - c DBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST

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.

Impacts of a Place-Based Science Curriculum on Student Place Attachment in Hawaiian and Western Cultural Institutions at an Urban High School in Hawai'i

Science.gov (United States)

Kuwahara, Jennifer L. H.

2013-01-01

This study investigates how students' participation in a place-based science curriculum may influence their place attachment (dependence and identity). Participants attend an urban high school in Hawai'i and are members of different cultural institutions within the school. Students are either enrolled in an environmental science class within the…

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.

Place in Transition

DEFF Research Database (Denmark)

Mikkelsen, Jacob Bjerre; Lange, Ida Sofie Gøtzsche

from the 'Everyday World'. Within mobilities studies, research has focused on different aspects and consequences of the post-oil society (see Dennis & Urry 2009, Urry 2013). This paper discusses the conception of place within the enclosed 'Oil World' with point of departure in relocation...... and redefinition of oil rigs from an urban design perspective. The paper constitutes a theoretical basis for future design scenarios - exemplified through visionary urban design proposals for a specific site in the city of Esbjerg, Denmark. Relocating rigs to an urban context initiates discussions of conception...... of 'Place' questioning the fixity of 'Place' (Jensen 2010). Scoped through a relational sense of place (Massey 1993) and the potential of exploring new relations between places (Burns & Kahn 2005), the paper challenges the notion of 'Place as God' (Hvattum 2010). These places in transition contest...

Time-series analysis to study the impact of an intersection on dispersion along a street canyon.

Science.gov (United States)

Richmond-Bryant, Jennifer; Eisner, Alfred D; Hahn, Intaek; Fortune, Christopher R; Drake-Richman, Zora E; Brixey, Laurie A; Talih, M; Wiener, Russell W; Ellenson, William D

2009-12-01

This paper presents data analysis from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study to assess the transport of ultrafine particulate matter (PM) across urban intersections. Experiments were performed in a street canyon perpendicular to a highway in Brooklyn, NY, USA. Real-time ultrafine PM samplers were positioned on either side of an intersection at multiple locations along a street to collect time-series number concentration data. Meteorology equipment was positioned within the street canyon and at an upstream background site to measure wind speed and direction. Time-series analysis was performed on the PM data to compute a transport velocity along the direction of the street for the cases where background winds were parallel and perpendicular to the street. The data were analyzed for sampler pairs located (1) on opposite sides of the intersection and (2) on the same block. The time-series analysis demonstrated along-street transport, including across the intersection when background winds were parallel to the street canyon and there was minimal transport and no communication across the intersection when background winds were perpendicular to the street canyon. Low but significant values of the cross-correlation function (CCF) underscore the turbulent nature of plume transport along the street canyon. The low correlations suggest that flow switching around corners or traffic-induced turbulence at the intersection may have aided dilution of the PM plume from the highway. This observation supports similar findings in the literature. Furthermore, the time-series analysis methodology applied in this study is introduced as a technique for studying spatiotemporal variation in the urban microscale environment.

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.

The Impact of Geographical Environment on Urban Place Names: Case of Şanlıurfa City

Directory of Open Access Journals (Sweden)

Veysi GÜNAL

2011-06-01

Full Text Available This study which holds Şanlıurfa city’s place names consists of three chapters. First, the factors that impact streets and district names are being held. Also, development of names is examined and classified. The purpose of this study is to understand the impact of geographical environment on naming of streets and districts. Natural environment characteristics have an impact on urban naming. City’s areal extension and an increase in population, national and local historical events, social and economic structure have an impact on urban place names too. While naming, the political party which includes mayor and councilors has an important mission. In this concept, there are a lot of places which have been named after person’s names, sites and historical-political events. Out of 149 names, 78 of it have taken its names after human activities. In the human activities based names (78, person’s names have an outstanding place (60. And in those names, governmental-military-political names (24 and religious person’s names come into prominence. In the city there are 22 natural environment names, 6 streets and 14 districts. There are a lot of urban places’ names which have been named after economic activities. These activities have given names to 36 districts and streets. Names which unknown the origin and others are 13

City Level of Income and Urbanization and Availability of Food Stores and Food Service Places in China.

Science.gov (United States)

Liao, Chunxiao; Tan, Yayun; Wu, Chaoqun; Wang, Shengfeng; Yu, Canqing; Cao, Weihua; Gao, Wenjing; Lv, Jun; Li, Liming

2016-01-01

The contribution of unhealthy dietary patterns to the epidemic of obesity has been well recognized. Differences in availability of foods may have an important influence on individual eating behaviors and health disparities. This study examined the availability of food stores and food service places by city characteristics on city level of income and urbanization. The cross-sectional survey was comprised of two parts: (1) an on-site observation to measure availability of food stores and food service places in 12 cities of China; (2) an in-store survey to determine the presence of fresh/frozen vegetables or fruits in all food stores. Trained investigators walked all the streets/roads within study tracts to identify all the food outlets. An observational survey questionnaire was used in all food stores to determine the presence of fresh/frozen vegetables or fruits. Urbanization index was determined for each city using a principal components factor analysis. City level of income and urbanization and numbers of each type of food stores and food service places were examined using negative binomial regression models. Large-sized supermarkets and specialty retailers had higher number of fresh/frozen vegetables or fruits sold compared to small/medium-sized markets. High-income versus low-income, high urbanized versus low urbanized areas had significantly more large-sized supermarkets and fewer small/medium-sized markets. In terms of restaurants, high urbanized cities had more western fast food restaurants and no statistically significant difference in the relative availability of any type of restaurants was found between high- and low-income areas. The findings suggested food environment disparities did exist in different cities of China.

City Level of Income and Urbanization and Availability of Food Stores and Food Service Places in China.

Directory of Open Access Journals (Sweden)

Chunxiao Liao

Full Text Available The contribution of unhealthy dietary patterns to the epidemic of obesity has been well recognized. Differences in availability of foods may have an important influence on individual eating behaviors and health disparities. This study examined the availability of food stores and food service places by city characteristics on city level of income and urbanization.The cross-sectional survey was comprised of two parts: (1 an on-site observation to measure availability of food stores and food service places in 12 cities of China; (2 an in-store survey to determine the presence of fresh/frozen vegetables or fruits in all food stores. Trained investigators walked all the streets/roads within study tracts to identify all the food outlets. An observational survey questionnaire was used in all food stores to determine the presence of fresh/frozen vegetables or fruits. Urbanization index was determined for each city using a principal components factor analysis. City level of income and urbanization and numbers of each type of food stores and food service places were examined using negative binomial regression models.Large-sized supermarkets and specialty retailers had higher number of fresh/frozen vegetables or fruits sold compared to small/medium-sized markets. High-income versus low-income, high urbanized versus low urbanized areas had significantly more large-sized supermarkets and fewer small/medium-sized markets. In terms of restaurants, high urbanized cities had more western fast food restaurants and no statistically significant difference in the relative availability of any type of restaurants was found between high- and low-income areas.The findings suggested food environment disparities did exist in different cities of China.

City Level of Income and Urbanization and Availability of Food Stores and Food Service Places in China

Science.gov (United States)

Liao, Chunxiao; Tan, Yayun; Wu, Chaoqun; Wang, Shengfeng; Yu, Canqing; Cao, Weihua; Gao, Wenjing; Lv, Jun; Li, Liming

2016-01-01

Objective The contribution of unhealthy dietary patterns to the epidemic of obesity has been well recognized. Differences in availability of foods may have an important influence on individual eating behaviors and health disparities. This study examined the availability of food stores and food service places by city characteristics on city level of income and urbanization. Methods The cross-sectional survey was comprised of two parts: (1) an on-site observation to measure availability of food stores and food service places in 12 cities of China; (2) an in-store survey to determine the presence of fresh/frozen vegetables or fruits in all food stores. Trained investigators walked all the streets/roads within study tracts to identify all the food outlets. An observational survey questionnaire was used in all food stores to determine the presence of fresh/frozen vegetables or fruits. Urbanization index was determined for each city using a principal components factor analysis. City level of income and urbanization and numbers of each type of food stores and food service places were examined using negative binomial regression models. Results Large-sized supermarkets and specialty retailers had higher number of fresh/frozen vegetables or fruits sold compared to small/medium-sized markets. High-income versus low-income, high urbanized versus low urbanized areas had significantly more large-sized supermarkets and fewer small/medium-sized markets. In terms of restaurants, high urbanized cities had more western fast food restaurants and no statistically significant difference in the relative availability of any type of restaurants was found between high- and low-income areas. Conclusions The findings suggested food environment disparities did exist in different cities of China. PMID:26938866

Network structure of multivariate time series.

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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

Automated Bayesian model development for frequency detection in biological time series

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Oldroyd Giles ED

2011-06-01

the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

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

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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

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

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

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Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

2009-08-01

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

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

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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.

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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

Educating Urban African American Children Placed at Risk: A Comparison of Two Types of Catholic Middle Schools

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Fenzel, L. Mickey; Domingues, Janine

2009-01-01

Although the number of urban Catholic schools has declined in recent years, Nativity model middle schools, first developed by the Jesuits over 35 years ago, have appeared throughout the nation to address the need for effective alternative education for urban children placed at risk. The present study compares the effectiveness of two types of…

Gap-filling of dry weather flow rate and water quality measurements in urban catchments by a time series modelling approach

DEFF Research Database (Denmark)

Sandoval, Santiago; Vezzaro, Luca; Bertrand-Krajewski, Jean-Luc

2016-01-01

seeks to evaluate the potential of the Singular Spectrum Analysis (SSA), a time-series modelling/gap-filling method, to complete dry weather time series. The SSA method is tested by reconstructing 1000 artificial discontinuous time series, randomly generated from real flow rate and total suspended......Flow rate and water quality dry weather time series in combined sewer systems might contain an important amount of missing data due to several reasons, such as failures related to the operation of the sensor or additional contributions during rainfall events. Therefore, the approach hereby proposed...... solids (TSS) online measurements (year 2007, 2 minutes time-step, combined system, Ecully, Lyon, France). Results show up the potential of the method to fill gaps longer than 0.5 days, especially between 0.5 days and 1 day (mean NSE > 0.6) in the flow rate time series. TSS results still perform very...

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...

A high-fidelity weather time series generator using the Markov Chain process on a piecewise level

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Hersvik, K.; Endrerud, O.-E. V.

2017-12-01

A method is developed for generating a set of unique weather time-series based on an existing weather series. The method allows statistically valid weather variations to take place within repeated simulations of offshore operations. The numerous generated time series need to share the same statistical qualities as the original time series. Statistical qualities here refer mainly to the distribution of weather windows available for work, including durations and frequencies of such weather windows, and seasonal characteristics. The method is based on the Markov chain process. The core new development lies in how the Markov Process is used, specifically by joining small pieces of random length time series together rather than joining individual weather states, each from a single time step, which is a common solution found in the literature. This new Markov model shows favorable characteristics with respect to the requirements set forth and all aspects of the validation performed.

Modelling urban travel times

NARCIS (Netherlands)

Zheng, F.

2011-01-01

Urban travel times are intrinsically uncertain due to a lot of stochastic characteristics of traffic, especially at signalized intersections. A single travel time does not have much meaning and is not informative to drivers or traffic managers. The range of travel times is large such that certain

Quantifying Urban Fragmentation under Economic Transition in Shanghai City, China

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Heyuan You

2015-12-01

Full Text Available Urban fragmentation affects sustainability through multiple impacts on economic, social, and environmental cost. Characterizing the dynamics of urban fragmentation in relation to economic transition should provide implications for sustainability. However, rather few efforts have been made in this issue. Using the case of Shanghai (China, this paper quantifies urban fragmentation in relation to economic transition. In particular, urban fragmentation is quantified by a time-series of remotely sensed images and a set of landscape metrics; and economic transition is described by a set of indicators from three aspects (globalization, decentralization, and marketization. Results show that urban fragmentation presents an increasing linear trend. Multivariate regression identifies positive linear correlation between urban fragmentation and economic transition. More specifically, the relative influence is different for the three components of economic transition. The relative influence of decentralization is stronger than that of globalization and marketization. The joint influences of decentralization and globalization are the strongest for urban fragmentation. The demonstrated methodology can be applicable to other places after making suitable adjustment of the economic transition indicators and fragmentation metrics.

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

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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.

Wildlife: a hidden treasure of green places in urbanized societies? : A study into whether and how wildlife contributes to a bond with green places among lay people in the Netherlands

NARCIS (Netherlands)

Folmer, Akke

2016-01-01

Wildlife: a hidden treasure of green places in urbanized societies In my thesis, I investigate how wildlife contributes to a bond with green places on different spatial scales among lay people in the Netherlands. The results show that wildlife matters in the bond with green places both near home,

Clustering of financial time series

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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.

Sowing Resilience and Contestation in Times of Crises: The Case of Urban Gardening Movements in Barcelona

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Marta Camps-Calvet

2015-07-01

Full Text Available Urban gardens have been observed to multiply in response to crises. However, the meaning and motivations behind the emergence of gardening movements varies greatly over space and time. In this paper we argue that bottom up urban gardening initiatives taking place in Southern European countries in form of land occupation and communalization represent forms of resistance that enhance social cohesion and collective action in times of need. Specifically, this research examines the role of urban gardens in (i building community resilience and (ii articulating forms of resistance and contestation to development pressure and commodified urban lifestyles. Our research is based on data collected among 27 urban gardening initiatives in Barcelona, Spain, including 13 self-governed community gardens and 14 public gardens. Data were collected from semi-structured interviews with gardeners and with staff from the Barcelona City Council. Our results show mechanisms through which urban gardens can contribute to build resilience by nurturing social and ecological diversity, generating and transmitting local ecological knowledge, and by creating opportunities for collective action and self-organization. We further examine collectively managed gardens as urban commons that emerge as a form of resistance to the privatization of public urban space, and that offer opportunities to experiment with new models of urban lifestyles. We show how gardening initiatives can be seen to represent an emerging form of urban green commons that provides a suitable ground to ‘sow’ resilience and contestation in times of crises and socio-ecological deterioration.

DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE

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H. Miyazaki

2016-06-01

Full Text Available Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC, was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

Data Mining Smart Energy Time Series

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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.

SimpleTimeseries: Towards a Standard Representation of Astronomical Time-Series

Science.gov (United States)

Brewer, John Michael; Bloom, J. S.; Starr, D.

2010-01-01

Centuries of astrophysical data will soon be eclipsed by the unprecedented number of novel events regularly captured by large scale synoptic surveys. In the past, a stately accumulation of data could await inclusion in catalogs. More recently, digital catalogs have been placed on websites, or forwarded in e-mails. Fully exploiting the science opportunities of this new era will require much more rapid and standardized data exchange. With abundant novel sources to choose from, the limited followup resources available will need regularized data formats to help in decision making, whether the ultimate decisions lie with a human or a machine. The Berkeley Transients Classification Pipeline (TCP) has developed an XML based time-series format to exchange data within the context of the Palomar Transients Factory (PTF). The benefit of a standard time-series representation lies in promulgating it beyond just one collaboration and so we are publicly releasing the format, SimpleTimeseries. It is also slated to describe time-series within the the Virtual Observatory's upcoming VOEvent 2.0 specification. An XML based format allows easy processing by both machines and humans. We have put together examples and documentation which show the flexibilty of SimpleTimeseries on dotastro.org, where you can also find the XML schema, and public light curves in the new format.

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

Measuring multiscaling in financial time-series

International Nuclear Information System (INIS)

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

2016-01-01

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

Time averaging, ageing and delay analysis of financial time series

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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)""…

Research on PM2.5 time series characteristics based on data mining technology

Science.gov (United States)

Zhao, Lifang; Jia, Jin

2018-02-01

With the development of data mining technology and the establishment of environmental air quality database, it is necessary to discover the potential correlations and rules by digging the massive environmental air quality information and analyzing the air pollution process. In this paper, we have presented a sequential pattern mining method based on the air quality data and pattern association technology to analyze the PM2.5 time series characteristics. Utilizing the real-time monitoring data of urban air quality in China, the time series rule and variation properties of PM2.5 under different pollution levels are extracted and analyzed. The analysis results show that the time sequence features of the PM2.5 concentration is directly affected by the alteration of the pollution degree. The longest time that PM2.5 remained stable is about 24 hours. As the pollution degree gets severer, the instability time and step ascending time gradually changes from 12-24 hours to 3 hours. The presented method is helpful for the controlling and forecasting of the air quality while saving the measuring costs, which is of great significance for the government regulation and public prevention of the air pollution.

MITRA Virtual laboratory for operative application of satellite time series for land degradation risk estimation

Science.gov (United States)

Nole, Gabriele; Scorza, Francesco; Lanorte, Antonio; Manzi, Teresa; Lasaponara, Rosa

2015-04-01

Geoinformation 2441-446 2. G Calamita, A Lanorte, R Lasaponara, B Murgante, G Nole 2013 Analyzing urban sprawl applying spatial autocorrelation techniques to multi-temporal satellite data. Urban and Regional Data Management: UDMS Annual 2013, 161 3. R Lasaponara 2013 Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation International Journal of Applied Earth Observations and Geoinformation 20 . 1-3 4. R Lasaponara, A Lanorte 2011 Satellite time-series analysis International Journal of Remote Sensing 33 (15), 4649-4652 5. G Nolè, M Danese, B Murgante, R Lasaponara, A Lanorte Using spatial autocorrelation techniques and multi-temporal satellite data for analyzing urban sprawl Computational Science and Its Applications-ICCSA 2012, 512-527

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.

Time series analysis of pressure fluctuation in gas-solid fluidized beds

Directory of Open Access Journals (Sweden)

C. Alberto S. Felipe

2004-09-01

Full Text Available The purpose of the present work was to study the differentiation of states of typical fluidization (single bubble, multiple bubble and slugging in a gas-solid fluidized bed, using spectral analysis of pressure fluctuation time series. The effects of the method of measuring (differential and absolute pressure fluctuations and the axial position of the probes in the fluidization column on the identification of each of the regimes studied were evaluated. Fast Fourier Transform (FFT was the mathematic tool used to analysing the data of pressure fluctuations, which expresses the behavior of a time series in the frequency domain. Results indicated that the plenum chamber was a place for reliable measurement and that care should be taken in measurement in the dense phase. The method allowed fluid dynamic regimes to be differentiated by their dominant frequency characteristics.

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....

Negotiating Sociolinguistic Borderlands--Native Youth Language Practices in Space, Time, and Place

Science.gov (United States)

McCarty, Teresa L.

2014-01-01

Drawing on the work of Philip Deloria (2004) and recent explorations of "American Indian languages in unexpected places" (Webster & Peterson, 2011a), this article challenges received expectations of Native American languages and language users as "rural" and physically distant and of "urban" Indigenous language…

Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components

Science.gov (United States)

Menzer, Olaf; McFadden, Joseph P.

2017-12-01

Eddy covariance flux measurements are increasingly used to quantify the net carbon dioxide exchange (FC) in urban areas. FC represents the sum of anthropogenic emissions, biogenic carbon release from plant and soil respiration, and carbon uptake by plant photosynthesis. When FC is measured in natural ecosystems, partitioning into respiration and photosynthesis is a well-established procedure. In contrast, few studies have partitioned FC at urban flux tower sites due to the difficulty of accounting for the temporal and spatial variability of the multiple sources and sinks. Here, we partitioned a three-year time series of flux measurements from a suburban neighborhood of Minneapolis-Saint Paul, Minnesota, USA. We segregated FC into one subset that captured fluxes from a residential neighborhood and into another subset that covered a golf course. For both land use types we modeled anthropogenic flux components based on winter data and extrapolated them to the growing season, to estimate gross primary production (GPP) and ecosystem respiration (Reco) at half-hourly, daily, monthly and annual scales. During the growing season, GPP had the largest magnitude (up to - 9.83 g C m-2 d-1) of any component CO2 flux, biogenic or anthropogenic, and both GPP and Reco were more dynamic seasonally than anthropogenic fluxes. Owing to the balancing of Reco against GPP, and the limitations of the growing season in a cold temperate climate zone, the net biogenic flux was only 1.5%-4.5% of the anthropogenic flux in the dominant residential land use type, and between 25%-31% of the anthropogenic flux in highly managed greenspace. Still, the vegetation sink at our site was stronger than net anthropogenic emissions on 16-20 days over the residential area and on 66-91 days over the recreational area. The reported carbon flux sums and dynamics are a critical step toward developing models of urban CO2 fluxes within and across cities that differ in vegetation cover.

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.

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…

Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

Directory of Open Access Journals (Sweden)

Leonardo Plazas-Nossa

2017-01-01

Full Text Available Context: The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, data pre-processing is a necessary pre-requisite to monitoring data processing. Thus, the aim of this study is to propose a method that detects and removes outliers as well as fills gaps in time series. Method: Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT and the Inverse of Fast Fourier Transform (IFFT to complete the time series. Together, these tools were used to analyse a case study comprising three sites in Colombia ((i Bogotá D.C. Salitre-WWTP (Waste Water Treatment Plant, influent; (ii Bogotá D.C. Gibraltar Pumping Station (GPS; and, (iii Itagüí, San Fernando-WWTP, influent (Medellín metropolitan area analysed via UV-Vis (Ultraviolet and Visible spectra. Results: Outlier detection with the proposed method obtained promising results when window parameter values are small and self-similar, despite that the three time series exhibited different sizes and behaviours. The DFT allowed to process different length gaps having missing values. To assess the validity of the proposed method, continuous subsets (a section of the absorbance time series without outlier or missing values were removed from the original time series obtaining an average 12% error rate in the three testing time series. Conclusions: The application of the DFT and the IFFT, using the 10% most important harmonics of useful values, can be useful for its later use in different applications, specifically for time series of water quality and quantity in urban sewer systems. One potential application would be the analysis of dry weather interesting to rain events, a feat achieved by detecting values that correspond to unusual behaviour in a time series. Additionally, the result hints at the potential of the method in correcting other hydrologic time series.

An empirical method for approximating stream baseflow time series using groundwater table fluctuations

Science.gov (United States)

Meshgi, Ali; Schmitter, Petra; Babovic, Vladan; Chui, Ting Fong May

2014-11-01

Developing reliable methods to estimate stream baseflow has been a subject of interest due to its importance in catchment response and sustainable watershed management. However, to date, in the absence of complex numerical models, baseflow is most commonly estimated using statistically derived empirical approaches that do not directly incorporate physically-meaningful information. On the other hand, Artificial Intelligence (AI) tools such as Genetic Programming (GP) offer unique capabilities to reduce the complexities of hydrological systems without losing relevant physical information. This study presents a simple-to-use empirical equation to estimate baseflow time series using GP so that minimal data is required and physical information is preserved. A groundwater numerical model was first adopted to simulate baseflow for a small semi-urban catchment (0.043 km2) located in Singapore. GP was then used to derive an empirical equation relating baseflow time series to time series of groundwater table fluctuations, which are relatively easily measured and are physically related to baseflow generation. The equation was then generalized for approximating baseflow in other catchments and validated for a larger vegetation-dominated basin located in the US (24 km2). Overall, this study used GP to propose a simple-to-use equation to predict baseflow time series based on only three parameters: minimum daily baseflow of the entire period, area of the catchment and groundwater table fluctuations. It serves as an alternative approach for baseflow estimation in un-gauged systems when only groundwater table and soil information is available, and is thus complementary to other methods that require discharge measurements.

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 Urbanism of Material

OpenAIRE

LAURA MARY HARPER

2018-01-01

This thesis investigates how the urban environment is constructed over time. The aim of this research is to understand the relationship between the decisions, logic and methods used at the scale of an individual site to the wider organisation and form of the urban environment. The thesis draws on the concept of bottom up systems to investigate ideas of collective organisation and characteristics in the urban environment. Using a series of architectural and urban case studies in Melbourne and ...

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...

URBAN RECONFIGURATIONS OF SPACE AND PLACE WITHIN TOWNSHIP TOURISM

Directory of Open Access Journals (Sweden)

Ana Craciunescu

2015-07-01

Full Text Available Nowadays global economic and cultural constellation determined urban communities to find a solution in order to preserve local identity and at the same time to attract capital into the area. Tourism represents in our opinion one of the greatest solutions ever exploited in mankind’s history which erases boundaries of nations and economic policies, creating glocalized encounters. In the case of a city, tourism or township tourism becomes an economical, political and cultural vector that unifies urban space which develops a network of genuine and artificial urban inter-relations between the principal stakeholders. The city as a destination must be a ‘safe’ construct that meets the expectations of various kinds of travellers and of their different travelling motivations. We believe that to a certain extent, the (rebranding of cities consists in the creation of a harmonized space that would reiterate the home-facilities of the traveller. A matter of life-style and life-quality, this issue will be analysed through the lens of travelling as a leisure activity, or as a way of escaping monotone routine of daily living, eventually a way of reinvesting income and creating economic equilibrium.

Together we have fun: native-place networks and sexual risk behaviours among Chinese male rural-urban migrants.

Science.gov (United States)

Yang, Xiaozhao Yousef; Kelly, Brian C; Yang, Tingzhong

2016-05-01

Some scholars argue that the maintenance of social networks contributes to the lower prevalence of deviant behaviours and fewer adverse health effects among migrants. But others suggest that if migrants are embedded in homogeneous networks, such networks may enable the formation of a deviant subculture that promotes risk taking. Facing this dilemma, the present study investigates how native-place networks influence sexual risk behaviours (SRBs), specifically the pursuit of commercial sex and condomless sex with sex workers, for male rural-urban migrants. Using a multi-stage sample of 1,591 male rural-urban migrants from two major migrant-influx cities within China, we assessed migrants' general friend network ties and native place networks (townsmen in migrants' local networks) and tested their associations with SRBs. Multiple logistic regression analyses indicate that native-place network ties are associated with paying for sex (OR = 1.33, p < 0.001) and condomless sex with sex workers (OR = 1.33, p < 0.001), while general friendship network ties reduce such risks (OR = 0.74, p < 0.001; OR = 0.84, p < 0.01) even after controlling for demographic background, housing conditions, length of stay, health beliefs and behaviours, and spousal companionship. Our findings suggest that native-place networks among Chinese male rural-urban migrants are associated with SRBs because homogenous networks may serve as a platform for the emergence of a deviant subculture that promotes risk behaviours. A Virtual Abstract of this paper is available at: https://www.youtube.com/watch?v=3Wg20I6j8XQ. © 2015 Foundation for the Sociology of Health & Illness.

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.

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...

Time- and place dependent tariffs

International Nuclear Information System (INIS)

Wangensteen, I.; Feilberg, N.; Hornnes, K.S.

1996-11-01

To study the variation of the marginal losses in the Norwegian regional and distribution networks, a stylized radial network and an existing network example were analyzed as described in this report. The main conclusion is that the marginal-cost (the marginal losses) varies with time and place in a way that is little reflected in the energy components of the transfer- and distribution tariffs. The difference between the actual marginal-cost at a given time at a given place and the transport price that confronts an actor through the tariffs is so large that one must ask if there is any point in basing a price on marginal-cost as long as today's calculation methods are used. The problem varies somewhat between the network levels. In the distribution network the range of variation is large within the same voltage level/tariff level. If the situation improves, a time differentiation is still required. A further improvement can be obtained by a place differentiation, for example by differentiation between densely and sparsely populated areas. However, this is difficult to realize. In the central network the problem is the same, but it is easier technically and administratively to arrive at a more correct arrangement. In practice there are no great problems in differentiating the price down to individual bus bars. This would relate input and output tariffs more correctly and logically. If time differentiation is intended to capture load variations, it seems that certain improvements are possible in the present classification. It appears that spring and autumn should stand apart as one period. Furthermore, the marginal loss tariff should be based on the water supply situation at the beginning of the tariff period. 10 refs., 13 figs., 17 tabs

Scale-dependent intrinsic entropies of complex time series.

Science.gov (United States)

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

2016-04-13

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

Elements of nonlinear time series analysis and forecasting

CERN Document Server

De Gooijer, Jan G

2017-01-01

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

An Energy-Based Similarity Measure for Time Series

Directory of Open Access Journals (Sweden)

Pierre Brunagel

2007-11-01

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

Detecting chaos in irregularly sampled time series.

Science.gov (United States)

Kulp, C W

2013-09-01

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

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

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

Property-driven Urban Change in Post-Socialist Shanghai: Reading the Television Series Woju

Directory of Open Access Journals (Sweden)

Samuel Y. Liang

2010-01-01

Full Text Available In late 2009, the television series Woju (蜗居 received extremely high audience ratings in major Chinese cities. Its visual narratives engage the public and comment on social developments by presenting detailed pictures of urban change in Shanghai and the everyday lives of a range of urban characters who are involved in and affected by the urban-restructuring process and represent three distinct social groups: “white-collar” immigrants, low-income local residents, and powerful officials. By analysing the visual narratives of these characters, this article highlights the loss of the city’s historical identity and shows how the reorganization of urban space translates into a reallocation of resources, power and prestige among the social groups. The article also shows that Woju repre-sents a new development in literary and television production in the age of the Internet and globalization; its imaginative construct of the city was based on transnational and virtual rather than local and neighbourhood experience. This also testifies to the loss of the city’s established identity in cultural production.

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...

Determining the Points of Change in Time Series of Polarimetric SAR Data

DEFF Research Database (Denmark)

Conradsen, Knut; Nielsen, Allan Aasbjerg; Skriver, Henning

2016-01-01

We present the likelihood ratio test statistic for the homogeneity of several complex variance–covariance matrices that may be used in order to assess whether at least one change has taken place in a time series of SAR data. Furthermore, we give a factorization of this test statistic into a produ....... The pixelwise analyses are applied on homogeneous subareas covered with different vegetation types using the distribution of the observed p-values....

Neural Network Models for Time Series Forecasts

OpenAIRE

Tim Hill; Marcus O'Connor; William Remus

1996-01-01

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

Time Series Observations in the North Indian Ocean

Digital Repository Service at National Institute of Oceanography (India)

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

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

A Philosophical Topography of Place and Non-Place: Lithuanian Context

Directory of Open Access Journals (Sweden)

Odeta Žukauskienė

2016-09-01

Full Text Available Drawing on French anthropologist Marc Augé and his seminal book Non-Places (1995 the author pays attention to the transformation of contemporary urban landscapes. In thinking trough the dialectic of place and non-place, this paper aims to account for the apparent sense of placelesness in our cultural landscapes and in increasingly globalised world. If we want to ask fundamental questions about what has happened to our urban landscape and to the spirit of cities during the last decades then the concepts of place and non-place help us to describe the actual changes. Besides, Augé’s work gives us the methodological tools to address philosophical questions about the nature of supermodernity and the relationship between modernity and postmodernity moving toward new conditions of globality. This article will attempt to apply anthropological and philosophical concepts of place and space to the context of Lithuania, comparing the ways of spreading of non-places (non-lieu in the Soviet modernity and contemporary global, hyper-visual and liquid cultural landscape.

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...

Road Traffic Injury Trends in the City of Valledupar, Colombia. A Time Series Study from 2008 to 2012.

Directory of Open Access Journals (Sweden)

Jorge Martín Rodríguez

Full Text Available To analyze the behavior temporal of road-traffic injuries (RTI in Valledupar, Colombia from January 2008 to December 2012.An observational study was conducted based on records from the Colombian National Legal Medicine and Forensic Sciences Institute regional office in Valledupar. Different variables were analyzed, such as the injured person's sex, age, education level, and type of road user; the timeframe, place and circumstances of crashes and the vehicles associated with the occurrence. Furthermore, a time series analysis was conducted using an auto-regressive integrated moving average.There were 105 events per month on an average, 64.9% of RTI involved men; 82.3% of the persons injured were from 18 to 59 years of age; the average age was 35.4 years of age; the road users most involved in RTI were motorcyclists (69%, followed by pedestrians (12%. 70% had up to upper-secondary education. Sunday was the day with the most RTI occurrences; 93% of the RTI occurred in the urban area. The time series showed a seasonal pattern and a significant trend effect. The modeling process verified the existence of both memory and extrinsic variables related.An RTI occurrence pattern was identified, which showed an upward trend during the period analyzed. Motorcyclists were the main road users involved in RTI, which suggests the need to design and implement specific measures for that type of road user, from regulations for graduated licensing for young drivers to monitoring road user behavior for the promotion of road safety.

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.

Understanding characteristics in multivariate traffic flow time series from complex network structure

Science.gov (United States)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

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.

Measuring small time periods in earth sciences by uranium series disequilibrium

International Nuclear Information System (INIS)

Choudhary, A.K.

2008-01-01

During the last three decades mass spectrometry in India has seen its application in almost every field of science. In particular, TIMS has revolutionized geological sciences by taking it from a mainly descriptive to modern quantitative Earth Sciences. It has largely contributed in measurement of precise time scales of geological processes. During the last decade, focus has primarily been on measurement of time scales of these fundamental processes. Some of the radiometric methods initially developed for measuring shorter time-scales have their own problems. The intermediate nuclides in the uranium and thorium decay series having much shorter half lives compared to their parents, provide a useful tool to measure intermediate time scales. These isotopes had earlier been ignored due to analytical difficulties associated with their measurement. The development of new generation mass spectrometers with very high abundance sensitivity has now made it possible to measure these isotopic ratios. Consequently U-series isotopic measurements have put unique and at times the only quantitative constraints on the processes taking place in the interior of the Earth. Since such mass spectrometers have recently been installed in some of the laboratories in India, scientific investigation may now be taken up in some of the unexplored areas of Earth Sciences in our country

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.

Population, migration and urbanization.

Science.gov (United States)

1982-06-01

Despite recent estimates that natural increase is becoming a more important component of urban growth than rural urban transfer (excess of inmigrants over outmigrants), the share of migration in the total population growth has been consistently increasing in both developed and developing countries. From a demographic perspective, the migration process involves 3 elements: an area of origin which the mover leaves and where he or she is considered an outmigrant; the destination or place of inmigration; and the period over which migration is measured. The 2 basic types of migration are internal and international. Internal migration consists of rural to urban migration, urban to urban migration, rural to rural migration, and urban to rural migration. Among these 4 types of migration various patterns or processes are followed. Migration may be direct when the migrant moves directly from the village to the city and stays there permanently. It can be circular migration, meaning that the migrant moves to the city when it is not planting season and returns to the village when he is needed on the farm. In stage migration the migrant makes a series of moves, each to a city closer to the largest or fastest growing city. Temporary migration may be 1 time or cyclical. The most dominant pattern of internal migration is rural urban. The contribution of migration to urbanization is evident. For example, the rapid urbanization and increase in urban growth from 1960-70 in the Republic of Korea can be attributed to net migration. In Asia the largest component of the population movement consists of individuals and groups moving from 1 rural location to another. Recently, because urban centers could no longer absorb the growing number of migrants from other places, there has been increased interest in the urban to rural population redistribution. This reverse migration also has come about due to slower rates of employment growth in the urban centers and improved economic opportunities

: Signal Decomposition of High Resolution Time Series River data to Separate Local and Regional Components of Conductivity

Science.gov (United States)

Signal processing techniques were applied to high-resolution time series data obtained from conductivity loggers placed upstream and downstream of a wastewater treatment facility along a river. Data was collected over 14-60 days, and several seasons. The power spectral densit...

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...

The role of time in place attachment

Science.gov (United States)

David Smaldone

2007-01-01

Quantitative studies have found that the length of association is an important variable affecting place attachment (Kaltenborn 1998, Moore & Graefe 1994, Patterson & Williams 1991, Vorkinn & Riese 2001). These studies, however, have provided less insight into how and why time is involved in the process of forming place attachment, as well as the meanings...

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.

Urbanization and stream ecology: Diverse mechanisms of change

Science.gov (United States)

Roy, Allison; Capps, Krista A.; El-Sabaawi, Rana W.; Jones, Krista L.; Parr, Thomas B.; Ramirez, Alonso; Smith, Robert F.; Walsh, Christopher J.; Wenger, Seth J.

2016-01-01

The field of urban stream ecology has evolved rapidly in the last 3 decades, and it now includes natural scientists from numerous disciplines working with social scientists, landscape planners and designers, and land and water managers to address complex, socioecological problems that have manifested in urban landscapes. Over the last decade, stream ecologists have met 3 times at the Symposium on Urbanization and Stream Ecology (SUSE) to discuss current research, identify knowledge gaps, and promote future research collaborations. The papers in this special series on urbanization and stream ecology include both primary research studies and conceptual synthesis papers spurred from discussions at SUSE in May 2014. The themes of the meeting are reflected in the papers in this series emphasizing global differences in mechanisms and responses of stream ecosystems to urbanization and management solutions in diverse urban streams. Our hope is that this series will encourage continued interdisciplinary and collaborative research to increase the global understanding of urban stream ecology toward stream protection and restoration in urban landscapes.

Time series prediction: statistical and neural techniques

Science.gov (United States)

Zahirniak, Daniel R.; DeSimio, Martin P.

1996-03-01

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

Effectiveness of Multivariate Time Series Classification Using Shapelets

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2015-01-01

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

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

International Nuclear Information System (INIS)

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

1982-05-01

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

Encounters in place ballet: a phenomenological perspective on older people’s walking routines in an urban park

NARCIS (Netherlands)

Eck, D. van; Pijpers, R.A.H.

2017-01-01

The phenomenological tradition within human geography continues to inspire research on everyday city life. This paper draws on David Seamon's notion of place ballet to understand the meaning of encounters between older people visiting an urban park in the city of Eindhoven, the Netherlands. The

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.

Signal Decomposition of High Resolution Time Series River Data to Separate Local and Regional Components of Conductivity

Science.gov (United States)

Signal processing techniques were applied to high-resolution time series data obtained from conductivity loggers placed upstream and downstream of an oil and gas wastewater treatment facility along a river. Data was collected over 14-60 days. The power spectral density was us...

An urban informatics approach to smart city learning in architecture and urban design education

Directory of Open Access Journals (Sweden)

Mirko Guaralda

2013-08-01

Full Text Available This study aims to redefine spaces of learning to places of learning through the direct engagement of local communities as a way to examine and learn from real world issues in the city. This paper exemplifies Smart City Learning, where the key goal is to promote the generation and exchange of urban design ideas for the future development of South Bank, in Brisbane, Australia, informing the creation of new design policies responding to the needs of local citizens. Specific to this project was the implementation of urban informatics techniques and approaches to promote innovative engagement strategies. Architecture and Urban Design students were encouraged to review and appropriate real-time, ubiquitous technology, social media, and mobile devices that were used by urban residents to augment and mediate the physical and digital layers of urban infrastructures. Our study’s experience found that urban informatics provide an innovative opportunity to enrich students’ place of learning within the city.

Spatiotemporal Patterns of Precipitation-Modulated Landslide Deformation From Independent Component Analysis of InSAR Time Series

Science.gov (United States)

Cohen-Waeber, J.; Bürgmann, R.; Chaussard, E.; Giannico, C.; Ferretti, A.

2018-02-01

Long-term landslide deformation is disruptive and costly in urbanized environments. We rely on TerraSAR-X satellite images (2009-2014) and an improved data processing algorithm (SqueeSAR™) to produce an exceptionally dense Interferometric Synthetic Aperture Radar ground deformation time series for the San Francisco East Bay Hills. Independent and principal component analyses of the time series reveal four distinct spatial and temporal surface deformation patterns in the area around Blakemont landslide, which we relate to different geomechanical processes. Two components of time-dependent landslide deformation isolate continuous motion and motion driven by precipitation-modulated pore pressure changes controlled by annual seasonal cycles and multiyear drought conditions. Two components capturing more widespread seasonal deformation separate precipitation-modulated soil swelling from annual cycles that may be related to groundwater level changes and thermal expansion of buildings. High-resolution characterization of landslide response to precipitation is a first step toward improved hazard forecasting.

Integer-valued time series

NARCIS (Netherlands)

van den Akker, R.

2007-01-01

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

Robust Forecasting of Non-Stationary Time Series

NARCIS (Netherlands)

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

2010-01-01

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

Participatory Climate Research in a Dynamic Urban Context: Activities of the Consortium for Climate Risk in the Urban Northeast (CCRUN)

Science.gov (United States)

Horton, Radley M.; Bader, Daniel A.; Montalto, Franco; Solecki, William

2016-01-01

The Consortium for Climate Risk in the Urban Northeast (CCRUN), one of ten NOAA-RISAs, supports resilience efforts in the urban corridor stretching from Philadelphia to Boston. Challenges and opportunities include the diverse set of needs in broad urban contexts, as well as the integration of interdisciplinary perspectives. CCRUN is addressing these challenges through strategies including: 1) the development of an integrated project framework, 2) stakeholder surveys, 3) leveraging extreme weather events as focusing opportunities, and 4) a seminar series that enables scientists and stakeholders to partner. While recognizing that the most extreme weather events will always lead to surprises (even with sound planning), CCRUN endeavors to remain flexible by facilitating place-based research in an interdisciplinary context.

Characterizing time series via complexity-entropy curves

Science.gov (United States)

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

2017-06-01

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

Complex network approach to fractional time series

Energy Technology Data Exchange (ETDEWEB)

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

2015-10-15

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

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.

PLACE IDENTITY IN 21ST CENTURY ARCHITECTURE IN SOUTH KOREA

Directory of Open Access Journals (Sweden)

Hee Sun (Sunny Choi

2011-11-01

Full Text Available Changes to the built environment brought about by economic and cultural globalization have resulted in a blurring of national identities worldwide. Consequently, place identity has emerged as a central concern for setting the 21st century urban development agenda. This paper examines the ways in which specific aspects of urban typology relate with cultural engagements and meanings within old and new, in terms of the transferable values of place identity, particularly within South East and Far East Asian countries. Firstly, the theoretical and practical key concepts for design ideology are described in relation to the value of place identity within contemporary urban forms. These key concepts are then operationalized in order to identify the implementation of the role of place identity, not only within architectural typology, but also through a cultural sense of space and time; a hybrid typological language. The focus of this paper is to explore how the role of place identity in physical built form relates with design qualities and cultural engagement, and how the needs of local culture can be incorporated, sustained and developed alongside contemporary architecture and rapid urban development. The paper provides a critical reflection and discussion of 21st Century architecture in South Korea, particularly how the locally situated and informed might be reconciled with the global aspirations of the contemporary city.

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.

Mid-IR spectrometer for mobile, real-time urban NO2 measurements

Science.gov (United States)

Morten Hundt, P.; Müller, Michael; Mangold, Markus; Tuzson, Béla; Scheidegger, Philipp; Looser, Herbert; Hüglin, Christoph; Emmenegger, Lukas

2018-05-01

Detailed knowledge about the urban NO2 concentration field is a key element for obtaining accurate pollution maps and individual exposure estimates. These are required for improving the understanding of the impact of ambient NO2 on human health and for related air quality measures. However, city-scale NO2 concentration maps with high spatio-temporal resolution are still lacking, mainly due to the difficulty of accurate measurement of NO2 at the required sub-ppb level precision. We contribute to close this gap through the development of a compact instrument based on mid-infrared laser absorption spectroscopy. Leveraging recent advances in infrared laser and detection technology and a novel circular absorption cell, we demonstrate the feasibility and robustness of this technique for demanding mobile applications. A fully autonomous quantum cascade laser absorption spectrometer (QCLAS) has been successfully deployed on a tram, performing long-term and real-time concentration measurements of NO2 in the city of Zurich (Switzerland). For ambient NO2 concentrations, the instrument demonstrated a precision of 0.23 ppb at one second time resolution and of 0.03 ppb after 200 s averaging. Whilst the combined uncertainty estimated for the retrieved spectroscopic values was less than 5 %, laboratory intercomparison measurements with standard CLD instruments revealed a systematic NO2 wall loss of about 10 % within the laser spectrometer. For the field campaign, the QCLAS has been referenced to a CLD using urban atmospheric air, despite the potential cross sensitivity of CLD to other nitrogen containing compounds. However, this approach allowed a direct comparison and continuous validation of the spectroscopic data to measurements at regulatory air quality monitoring (AQM) stations along the tram-line. The analysis of the recorded high-resolution time series allowed us to gain more detailed insights into the spatio-temporal concentration distribution of NO2 in an urban

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...

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.

Turn and jump: how time & place fell apart

CERN Document Server

Mansfield, Howard

2013-01-01

Before Thomas Edison, light and fire were thought to be one and the same. Turns out, they were separate things altogether. This book takes a similar relationship, that of time and place, and shows how they, too, were once inseparable. Time keeping was once a local affair, when small towns set their own pace according to the rising and setting of the sun. Then, in 1883, the expanding railroads necessitated the creation of Standard Time zones, and communities became linked by a universal time. Here Howard Mansfield explores how our sudden interconnectedness, both physically, as through the railroad, and through inventions like the telegraph, changed our concept of time and place forever.

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.

Extracting Knowledge From Time Series An Introduction to Nonlinear Empirical Modeling

CERN Document Server

Bezruchko, Boris P

2010-01-01

This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolution. In particular, it teaches readers how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sciences who seek a self-contained and thorough introduction to this subject.

Efficient Approximate OLAP Querying Over Time Series

DEFF Research Database (Denmark)

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

2016-01-01

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

A Dynamic Fuzzy Cluster Algorithm for Time Series

Directory of Open Access Journals (Sweden)

Min Ji

2013-01-01

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

Importance of Managing for Personal Benefits, Hedonic and Utilitarian Motivations, and Place Attachment at an Urban Natural Setting.

Science.gov (United States)

Budruk, Megha; Lee, Woojin

2016-09-01

Research on antecedents of place attachment suggests that the special bonds people form with nature are influenced by a number of variables. This study examines associations between the perceived importance of managing for personal benefits, motivations, and place attachment among outdoor recreationists at an urban natural setting. Motivation was conceptualized as two-dimensional (Hedonic and Utilitarian) borrowed from the retail and consumer marketing field and previously unused in a natural resource recreation context. Hedonic and utilitarian motivations represent the experiential and functional dimensions of motivation, respectively. Relationships between the noted variables were examined through structural equation modeling. Data from an onsite survey of 219 users indicated that it was important the resource be managed to provide greater freedom from urban living as well as improved mental well-being. Furthermore, respondents exhibited moderate levels of hedonic and utilitarian motivations as well as attachment to the resource. The structural equation analysis resulted in a good fitting model with several significant relationships emerging. Among these, the perceived importance of managing for personal benefits positively influenced hedonic and utilitarian motivations. In addition, hedonic motivations positively influenced place attachment development, whereas utilitarian motivations did not. Implications of these findings are discussed.

Importance of Managing for Personal Benefits, Hedonic and Utilitarian Motivations, and Place Attachment at an Urban Natural Setting

Science.gov (United States)

Budruk, Megha; Lee, Woojin

2016-09-01

Research on antecedents of place attachment suggests that the special bonds people form with nature are influenced by a number of variables. This study examines associations between the perceived importance of managing for personal benefits, motivations, and place attachment among outdoor recreationists at an urban natural setting. Motivation was conceptualized as two-dimensional (Hedonic and Utilitarian) borrowed from the retail and consumer marketing field and previously unused in a natural resource recreation context. Hedonic and utilitarian motivations represent the experiential and functional dimensions of motivation, respectively. Relationships between the noted variables were examined through structural equation modeling. Data from an onsite survey of 219 users indicated that it was important the resource be managed to provide greater freedom from urban living as well as improved mental well-being. Furthermore, respondents exhibited moderate levels of hedonic and utilitarian motivations as well as attachment to the resource. The structural equation analysis resulted in a good fitting model with several significant relationships emerging. Among these, the perceived importance of managing for personal benefits positively influenced hedonic and utilitarian motivations. In addition, hedonic motivations positively influenced place attachment development, whereas utilitarian motivations did not. Implications of these findings are discussed.

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...

A modified temporal criterion to meta-optimize the extended Kalman filter for land cover classification of remotely sensed time series

Science.gov (United States)

Salmon, B. P.; Kleynhans, W.; Olivier, J. C.; van den Bergh, F.; Wessels, K. J.

2018-05-01

Humans are transforming land cover at an ever-increasing rate. Accurate geographical maps on land cover, especially rural and urban settlements are essential to planning sustainable development. Time series extracted from MODerate resolution Imaging Spectroradiometer (MODIS) land surface reflectance products have been used to differentiate land cover classes by analyzing the seasonal patterns in reflectance values. The proper fitting of a parametric model to these time series usually requires several adjustments to the regression method. To reduce the workload, a global setting of parameters is done to the regression method for a geographical area. In this work we have modified a meta-optimization approach to setting a regression method to extract the parameters on a per time series basis. The standard deviation of the model parameters and magnitude of residuals are used as scoring function. We successfully fitted a triply modulated model to the seasonal patterns of our study area using a non-linear extended Kalman filter (EKF). The approach uses temporal information which significantly reduces the processing time and storage requirements to process each time series. It also derives reliability metrics for each time series individually. The features extracted using the proposed method are classified with a support vector machine and the performance of the method is compared to the original approach on our ground truth data.

Infrastructural urbanism that learns from place

DEFF Research Database (Denmark)

Carruth, Susan

2015-01-01

. Conventionally, energy ‘infrastructure’ denotes a physical system of pipes, cables, generators, plants, transformers, sockets, and pylons, however recent architectural research emerging within the loosely defined movement of Infrastructural Urbanism has reframed infrastructure as a symbiotic system of flows...

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.

Converging social classes through humanized urban edges

Science.gov (United States)

Abuan, M. V.; Galingan, Z. D.

2017-10-01

Urban open spaces are created to be used by people. It is a place of convergence and social activity. However, these places have transformed into places of divergence. When spaces become dehumanized, it separates social classes. As a result, underused spaces contribute to urban decay. Particularly an urban edge, the JP Rizal Makati Waterfront Area is the center of this paper. The JP Rizal Makati Waterfront Area is a waterfront development situated along the banks of one of Metro Manila’s major water thoroughfare --- Pasig River. The park and its physical form, urban design and landscape tend to deteriorate over time --- creating a further division of social convergence. Social hostility, crime, negligent maintenance and poor urban design are contributing factors to this sprawling decay in what used to be spaces of bringing people together. Amidst attempts to beautify and renew this portion of Makati City’s edge, the urban area still remains misspent.This paper attempts to re-humanize the waterfront development. It uses the responsive environment design principles to be able to achieve this goal.

Taking place, screening place

DEFF Research Database (Denmark)

Hansen, Kim Toft; Waade, Anne Marit

2019-01-01

We introduce location studies as a new empirical approach to screen studies. Location studies represent an interdisciplinary perspective, including media, aesthetics and geography, and reflect a growing interest in places in a global media and consumption culture. The chapter analyses two recent......) with one being traditional and the other being commercial; both dramas include discussions of localities and social heritage, and both use local sports as a common metaphor for social cohesion; and both series have been partly funded by a local film Danish commissioner. However, The Legacy is shot...... to a large extent in studios, while Norskov is shot entirely on location. The study is based on interviews with producers, broadcasters, location scouts, production designers and writers, as well as quantitative and qualitative textual analyses of television drama series, the geographical places, and related...

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.

Urban-Rural Problems. Contemporary Social Problems Series.

Science.gov (United States)

Taylor, Lee

Various social problems are created by migration of low-income rural people into urban areas. These people are classified "low income" because their material level-of-living is often less than that found in urban areas. The dominant national values for material well-being are based upon urban middle class standards, thus creating a social problem…

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.

UrbanTransformation

DEFF Research Database (Denmark)

Laursen, Lea Louise Holst

Due to the economical and political changes marked by globalization, neo-liberalism and, post-industrialism a changed spatial configuration is emerging in which an increased division is taking place, into on the one hand, economical and demographical growing urban areas, where the urban fabric...... is being concentrated, and on the other, into declining urban areas that experience a dilution of the urban fabric and a de-concentration of people and capital. This gives an uneven spatial geography where some places are becoming nodal points in the global society and others are left behind. But the urban...... situation of concentration and de-concentration is also closely connected where there is a dynamic relation between the two. Decline might in some cases even be seen as an aspect of growth, where the growth of some places influence the decline in others. With this approach the urban fabric can, therefore...

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").

Baltimore in The Wire and Los Angeles in The Shield: Urban Landscapes in American Drama Series

Directory of Open Access Journals (Sweden)

Alberto N. García

2017-07-01

Full Text Available The Shield (FX 2002-08 and The Wire (HBO 2002-08 are two of the most ever critically acclaimed TV-shows and they both can be seen as the finest developed film noir proposals produced in television. The Wire transcends the cop-show genre by offering a multilayered portrait of the whole city of Baltimore: from police work to drug dealing, getting through stevedores’ union corruption, tricks of local politics, problems of the school system and some unethical journalism practices. On the other, The Shield offers a breathtaking cop-show that features in the foreground the moral ambiguity that characterizes the noir genre. Both series display complementary realist strategies (a neorealist aesthetic in The Wire; a cinéma vérité pastiche in The Shield that highlight the importance of city landscape in their narrative. Baltimore and Los Angeles are portrayed not only as a dangerous and ruined physical places, but are also intertwined with moral and political issues in contemporary cities, such as race, class, political corruption, social disintegration, economical disparities, the limitations of the system of justice, the failure of the American dream and so on. The complex and expanded narrative of The Wire and The Shield, as Dimemberg has written for film noir genre, “remains well attuned to the violently fragmented spaces and times of the late-modern world”. Therefore, this article will focus on how The Wire and The Shield (and some of their TV heirs, such as Southland and Justified reflect and renew several topics related to the city in the film noir tradition: the sociopolitical effects of showing the ruins of the centripetal industrial metropolis, the inferences of filming in actual places, the dramatic presence of what Augé coined as “no-places”, the bachelardian opposition between home and city, or the streets as an urban jungle where danger lurks in every corner.

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.

Connecting people and place: a new framework for reducing urban vulnerability to extreme heat

International Nuclear Information System (INIS)

Wilhelmi, Olga V; Hayden, Mary H

2010-01-01

Climate change is predicted to increase the intensity and negative impacts of urban heat events, prompting the need to develop preparedness and adaptation strategies that reduce societal vulnerability to extreme heat. Analysis of societal vulnerability to extreme heat events requires an interdisciplinary approach that includes information about weather and climate, the natural and built environment, social processes and characteristics, interactions with stakeholders, and an assessment of community vulnerability at a local level. In this letter, we explore the relationships between people and places, in the context of urban heat stress, and present a new research framework for a multi-faceted, top-down and bottom-up analysis of local-level vulnerability to extreme heat. This framework aims to better represent societal vulnerability through the integration of quantitative and qualitative data that go beyond aggregate demographic information. We discuss how different elements of the framework help to focus attention and resources on more targeted health interventions, heat hazard mitigation and climate adaptation strategies.

Place in transition

DEFF Research Database (Denmark)

Mikkelsen, Jacob Bjerre; Lange, Ida Sofie Gøtzsche

2017-01-01

World. This paper discusses the conception of place in the Oil World, with the relocation and transformation of oil rigs from an urban design perspective as its point of departure, using Esbjerg, Denmark, as a case study. Combining a theoretical understanding of places as relational with a design...

Urban Insertions and Landscape Visions. Tension between Design and Place in the Cemeteries by Sigurd Lewerentz

Directory of Open Access Journals (Sweden)

Carlotta Torricelli

2015-12-01

Full Text Available Designing memorial places involves a reflection about the Origin. Starting from this premise, the paper illustrates some small cemeteries designed by Sigurd Lewerentz in the same years when he was working at the two celebrated sacred spaces of Enskede (Stockholm and East-Malmö. The work developed by the Swedish architect in Forsbacka, Valdemarsvik, Rud and Kvarnsveden shows a peculiar approach aiming to reveal the character of the place. Lewerentz, through the landscape design, gives the ground – seen as a factor of origin – an evocative value. Using signs that allude to archetypes of the relationship between man and the divine, Lewerentz deploys natural features along with artificial and abstract elements. The pursuit of a sense of origin settles the project into the place, and in this we can recognize a founding principle able to orient contemporary urban projects.

HEALTH GeoJunction: place-time-concept browsing of health publications.

Science.gov (United States)

MacEachren, Alan M; Stryker, Michael S; Turton, Ian J; Pezanowski, Scott

2010-05-18

The volume of health science publications is escalating rapidly. Thus, keeping up with developments is becoming harder as is the task of finding important cross-domain connections. When geographic location is a relevant component of research reported in publications, these tasks are more difficult because standard search and indexing facilities have limited or no ability to identify geographic foci in documents. This paper introduces HEALTH GeoJunction, a web application that supports researchers in the task of quickly finding scientific publications that are relevant geographically and temporally as well as thematically. HEALTH GeoJunction is a geovisual analytics-enabled web application providing: (a) web services using computational reasoning methods to extract place-time-concept information from bibliographic data for documents and (b) visually-enabled place-time-concept query, filtering, and contextualizing tools that apply to both the documents and their extracted content. This paper focuses specifically on strategies for visually-enabled, iterative, facet-like, place-time-concept filtering that allows analysts to quickly drill down to scientific findings of interest in PubMed abstracts and to explore relations among abstracts and extracted concepts in place and time. The approach enables analysts to: find publications without knowing all relevant query parameters, recognize unanticipated geographic relations within and among documents in multiple health domains, identify the thematic emphasis of research targeting particular places, notice changes in concepts over time, and notice changes in places where concepts are emphasized. PubMed is a database of over 19 million biomedical abstracts and citations maintained by the National Center for Biotechnology Information; achieving quick filtering is an important contribution due to the database size. Including geography in filters is important due to rapidly escalating attention to geographic factors in public

HEALTH GeoJunction: place-time-concept browsing of health publications

Directory of Open Access Journals (Sweden)

Turton Ian J

2010-05-01

Full Text Available Abstract Background The volume of health science publications is escalating rapidly. Thus, keeping up with developments is becoming harder as is the task of finding important cross-domain connections. When geographic location is a relevant component of research reported in publications, these tasks are more difficult because standard search and indexing facilities have limited or no ability to identify geographic foci in documents. This paper introduces HEALTH GeoJunction, a web application that supports researchers in the task of quickly finding scientific publications that are relevant geographically and temporally as well as thematically. Results HEALTH GeoJunction is a geovisual analytics-enabled web application providing: (a web services using computational reasoning methods to extract place-time-concept information from bibliographic data for documents and (b visually-enabled place-time-concept query, filtering, and contextualizing tools that apply to both the documents and their extracted content. This paper focuses specifically on strategies for visually-enabled, iterative, facet-like, place-time-concept filtering that allows analysts to quickly drill down to scientific findings of interest in PubMed abstracts and to explore relations among abstracts and extracted concepts in place and time. The approach enables analysts to: find publications without knowing all relevant query parameters, recognize unanticipated geographic relations within and among documents in multiple health domains, identify the thematic emphasis of research targeting particular places, notice changes in concepts over time, and notice changes in places where concepts are emphasized. Conclusions PubMed is a database of over 19 million biomedical abstracts and citations maintained by the National Center for Biotechnology Information; achieving quick filtering is an important contribution due to the database size. Including geography in filters is important due to

Probabilistic modelling of overflow, surcharge and flooding in urban drainage using the first-order reliability method and parameterization of local rain series.

Science.gov (United States)

Thorndahl, S; Willems, P

2008-01-01

Failure of urban drainage systems may occur due to surcharge or flooding at specific manholes in the system, or due to overflows from combined sewer systems to receiving waters. To quantify the probability or return period of failure, standard approaches make use of the simulation of design storms or long historical rainfall series in a hydrodynamic model of the urban drainage system. In this paper, an alternative probabilistic method is investigated: the first-order reliability method (FORM). To apply this method, a long rainfall time series was divided in rainstorms (rain events), and each rainstorm conceptualized to a synthetic rainfall hyetograph by a Gaussian shape with the parameters rainstorm depth, duration and peak intensity. Probability distributions were calibrated for these three parameters and used on the basis of the failure probability estimation, together with a hydrodynamic simulation model to determine the failure conditions for each set of parameters. The method takes into account the uncertainties involved in the rainstorm parameterization. Comparison is made between the failure probability results of the FORM method, the standard method using long-term simulations and alternative methods based on random sampling (Monte Carlo direct sampling and importance sampling). It is concluded that without crucial influence on the modelling accuracy, the FORM is very applicable as an alternative to traditional long-term simulations of urban drainage systems.

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.

Multivariate autoregressive modelling and conditional simulation of precipitation time series for urban water models

NARCIS (Netherlands)

Torres-Matallana, J.A.; Leopold, U.; Heuvelink, G.B.M.

2017-01-01

Precipitation is the most active flux and major input of hydrological systems. Precipitation controls hydrological states (soil moisture and groundwater level), and fluxes (runoff, evapotranspiration and groundwater recharge).
Hence, precipitation plays a paramount role in urban water systems.

Real time urbanism

Directory of Open Access Journals (Sweden)

Ana Ruiz Varona

2012-12-01

Full Text Available Nowadays, given the technological revolution of the society of information, the administrative management of the cities faces a new problem not as related to the projection of the urban space as to the capacity of controlling and measuring the process of direct and centralized production of the cities by part of some non-homogeneous social multitudes, in a hyper-accelerated time towards instantaneity. Against libertarian apologies of the new “participative urbanisms”, the article puts forward a discourse that shows the lost associated to the new problem of temporal instantaneity. In this regard we claim new process of mediation that allow administrations and urbanist monitoring the production of the city. To that end, a previous and necessary step will be the redefinition of the role of a new real time urbanist.

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.

Interdisciplinary Pathways for Urban Metabolism Research

Science.gov (United States)

Newell, J. P.

2011-12-01

With its rapid rise as a metaphor to express coupled natural-human systems in cities, the concept of urban metabolism is evolving into a series of relatively distinct research frameworks amongst various disciplines, with varying definitions, theories, models, and emphases. In industrial ecology, housed primarily within the disciplinary domain of engineering, urban metabolism research has focused on quantifying material and energy flows into, within, and out of cities, using methodologies such as material flow analysis and life cycle assessment. In the field of urban ecology, which is strongly influenced by ecology and urban planning, research focus has been placed on understanding and modeling the complex patterns and processes of human-ecological systems within urban areas. Finally, in political ecology, closely aligned with human geography and anthropology, scholars theorize about the interwoven knots of social and natural processes, material flows, and spatial structures that form the urban metabolism. This paper offers three potential interdisciplinary urban metabolism research tracks that might integrate elements of these three "ecologies," thereby bridging engineering and the social and physical sciences. First, it presents the idea of infrastructure ecology, which explores the complex, emergent interdependencies between gray (water and wastewater, transportation, etc) and green (e.g. parks, greenways) infrastructure systems, as nested within a broader socio-economic context. For cities to be sustainable and resilient over time-space, the theory follows, these is a need to understand and redesign these infrastructure linkages. Second, there is the concept of an urban-scale carbon metabolism model which integrates consumption-based material flow analysis (including goods, water, and materials), with the carbon sink and source dynamics of the built environment (e.g. buildings, etc) and urban ecosystems. Finally, there is the political ecology of the material

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...

Socio-technical Systems as Place-specific Matters of Concern

DEFF Research Database (Denmark)

Jensen, J. S.; Fratini, C. F.; Cashmore, M. A.

2016-01-01

that urban governance of the wastewater system was influenced by a particular concern with developing attractive and competitive urban spaces. The wastewater system emerged as a ‘place-bound’ and even ‘place-making’ governance concern; as such, the boundaries and functions of the system were subject...... to continuous redefinition at the city level. This urban framing conflicted with the national-level, efficiency-oriented framing of the wastewater system as homogenous, without regard to place-specific differences. The research findings suggest that a distinct characteristic of urban-level governance is concern...... for place-specific development; this concern can be transformative because it leads to ongoing reinterpretation of traditional boundaries and dependencies between large-scale systems and local contexts....

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.

URBAN TOURISM BETWEEN CONTENT AND ASPIRATION FOR URBAN DEVELOPMENT

Directory of Open Access Journals (Sweden)

Roxana Valentina GÂRBEA

2013-06-01

Full Text Available With excessive urbanization that the society knows today, the city became the place of origin and at the same time a destination for an increasingly number of tourists. Cities have a higher fitting territory, diversity and quality of tourism products coming to fill a reach touristic ground, especially anthropogenic. Urban tourism has seen a significant, but uneven increase, whit the big European cities detaching themselves through cultural richness and tourist valorization of urban space and may be role models for other cities. The article proposes the approach on the concept of urban tourism and how this form of tourism is in full process of affirmation, given that, in recent years many cities search to find a new identity for themselves, to gain international recognition through tourism.

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...

Modeling time-dependent toxicity to aquatic organisms from pulsed exposure of PAHs in urban road runoff

International Nuclear Information System (INIS)

Zhang Wei; Ye Youbin; Tong Yindong; Ou Langbo; Hu Dan; Wang Xuejun

2011-01-01

Understanding of the magnitude of urban runoff toxicity to aquatic organisms is important for effective management of runoff quality. In this paper, the aquatic toxicity of polycyclic aromatic hydrocarbons (PAHs) in urban road runoff was evaluated through a damage assessment model. Mortality probability of the organisms representative in aquatic environment was calculated using the monitored PAHs concentration in road runoff. The result showed that the toxicity of runoff in spring was higher than those in summer. Analysis of the time-dependent toxicity of series of runoff water samples illustrated that the toxicity of runoff water in the final phase of a runoff event may be as high as those in the initial phase. Therefore, the storm runoff treatment systems or strategies designed for capture and treatment of the initial portion of runoff may be inappropriate for control of runoff toxicity. - Research highlights: → Toxicity resulting from realistic exposure patterns of urban runoff is evaluated. → Toxicity of runoff water in the final phase is as high as the initial phase. → Treatment of the initial runoff portion is inappropriate to abate runoff toxicity. - Toxicity to aquatic organisms after sequential pulsed exposure to PAHs in urban road runoff is evaluated.

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

Socio-Cultural Impacts in the Formation of Urban Village

Science.gov (United States)

Marpaung, B. O. Y.

2017-03-01

In Indonesia, a group of village people tends to move from one place to another and develops a living space to create a settlement. This research is conducted by taking an example of a particular ethnic group that leaves the forestry area to a new place in the city. After some time, this group of people creates a similar or adapted socio-cultural system adapted from their origin place. The purpose of this research is to examine the socio-cultural aspects that significantly influence the emergence of urban village. This influence is interpreted as social and cultural relations with the establishment of space and significance of urban village. By focusing on this issue, this research will trace the process of how a new and unplanned settlement could emerge. The process and elements are indispensable from social and cultural factors. Essentially, the shape of bulit space is a non-physical manifestation of local people, which is established from time to time. In this case, the research’s challenge lies on the circumstance in Indonesia where society and culture influence the emergence of urban village. Physical appearance can be identified as a tipology of settlement and morphology of urban village.

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...

Evidence for an alternation strategy in time-place learning.

Science.gov (United States)

Pizzo, Matthew J; Crystal, Jonathon D

2004-11-30

Many different conclusions concerning what type of mechanism rats use to solve a daily time-place task have emerged in the literature. The purpose of this study was to test three competing explanations of time-place discrimination. Rats (n = 10) were tested twice daily in a T-maze, separated by approximately 7 h. Food was available at one location in the morning and another location in the afternoon. After the rats learned to visit each location at the appropriate time, tests were omitted to evaluate whether the rats were utilizing time-of-day (i.e., a circadian oscillator) or an alternation strategy (i.e., visiting a correct location is a cue to visit the next location). Performance on this test was significantly lower than chance, ruling out the use of time-of-day. A phase advance of the light cycle was conducted to test the alternation strategy and timing with respect to the light cycle (i.e., an interval timer). There was no difference between probe and baseline performance. These results suggest that the rats used an alternation strategy to meet the temporal and spatial contingencies in the time-place task.

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...

Higher Education and Urban Migration for Community Resilience: Indigenous Amazonian Youth Promoting Place-Based Livelihoods and Identities in Peru

Science.gov (United States)

Steele, Diana

2018-01-01

This paper offers an ethnographic analysis of indigenous Peruvian Amazonian youth pursuing higher education through urban migration to contribute to the resilience of their communities, place-based livelihoods, and indigenous Amazonian identities. Youth and their communities promoted education and migration as powerful tools in the context of…

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.

Electropollution in our urban environment

Directory of Open Access Journals (Sweden)

Alina Cobzaru

2015-06-01

Full Text Available The paper is a descriptive study based on the scientific results coming out from all over the world in the last years (2007-2013, concerning an increasing level of electropollution in our urban envi-ronment as a dangerous health threat mostly in the big and crowded urban places. Electropollution is today subject of serious health damages. The paper mention results of several significant studies including the BioInitiative Report 2012 prepared by 29 authors from ten countries, and covers a series of questions in this field, in our country to raise awareness of specialists in architecture and constructions services, if we may have designed sustainable, elegant, functionally efficient building com-plexes, without a prolonged exposure to radiofrequency and microwave ra-diations coming from the emitting modern technology.

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.

Identifying the driving forces of urban expansion and its environmental impact in Jakarta-Bandung mega urban region

Science.gov (United States)

Pravitasari, A. E.; Rustiadi, E.; Mulya, S. P.; Setiawan, Y.; Fuadina, L. N.; Murtadho, A.

2018-05-01

The socio-economic development in Jakarta-Bandung Mega Urban Region (JBMUR) caused the increasing of urban expansion and led to a variety of environmental damage such as uncontrolled land use conversion and raising anthropogenic disaster. The objectives of this study are: (1) to identify the driving forces of urban expansion that occurs on JBMUR and (2) to analyze the environmental quality decline on JBMUR by producing time series spatial distribution map and spatial autocorrelation of floods and landslide as the proxy of anthropogenic disaster. The driving forces of urban expansion in this study were identified by employing Geographically Weighted Regression (GWR) model using 6 (six) independent variables, namely: population density, percentage of agricultural land, distance to the center of capital city/municipality, percentage of household who works in agricultural sector, distance to the provincial road, and distance to the local road. The GWR results showed that local demographic, social and economic factors including distance to the road spatially affect urban expansion in JBMUR. The time series spatial distribution map of floods and landslide event showed the spatial cluster of anthropogenic disaster in some areas. Through Local Moran Index, we found that environmental damage in one location has a significant impact on the condition of its surrounding area.

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 ...

The end of Place as We Know It? Attempts at Conceptualization

Directory of Open Access Journals (Sweden)

Małgorzata Dymnicka

2010-05-01

Full Text Available This article is an attempt to join in the ongoing in recent years debate on the conceptualization of place in times of global changes, which on the one hand have freed the place from its limitation and confinement, and on the other hand have weakened its basic function of social creation of meanings, of sense of community, of existential security, of ingraining in and of trust. In the situation of the increasingly more frequent appropriation of the places by corporations, means of transportation and information, the ideal of place, although it departs from its primary physicality moving to the sphere of imagination effectively fuelled by the marketing industry, still remains the fundamental environment of most people’s everyday life. I believe that a challenge of our times is a revision of various circles’ attitudes towards negligence of everyday culture, whose significant part seems to be the place – a live tissue of social relationships and collective identities. However, some architects and sociologists underline unanimously that there is interdependence between designing the urban environment and social production of place. From the sociological point of view place is more than just the urban form. The places have to be conceived as the collection of variously interrelated spaces (physically and virtually, enabling different activities. In the times of globalization, the places do not disappear from the surface of the earth, but they rather undergo the reconfiguration process that consists in developing the exchange with the other places. The selection of the issues in the text, of which the author is fully aware, does not exhaust the discussed problems, although they are not chosen at random.

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.

Envisioning place

DEFF Research Database (Denmark)

Schmidt, Garbi; Glick Schiller, Nina

2016-01-01

together, the articles contribute to an emerging relational social science by approaching urban sociabilities through four interrelated parameters: (1) a concept of place-making situated within trajectories of differential and multiscalar power; (2) a discursive analysis of narratives and silences...

Remote Sensing of River Delta Inundation: Exploiting the Potential of Coarse Spatial Resolution, Temporally-Dense MODIS Time Series

Directory of Open Access Journals (Sweden)

Claudia Kuenzer

2015-07-01

Full Text Available River deltas belong to the most densely settled places on earth. Although they only account for 5% of the global land surface, over 550 million people live in deltas. These preferred livelihood locations, which feature flat terrain, fertile alluvial soils, access to fluvial and marine resources, a rich wetland biodiversity and other advantages are, however, threatened by numerous internal and external processes. Socio-economic development, urbanization, climate change induced sea level rise, as well as flood pulse changes due to upstream water diversion all lead to changes in these highly dynamic systems. A thorough understanding of a river delta’s general setting and intra-annual as well as long-term dynamic is therefore crucial for an informed management of natural resources. Here, remote sensing can play a key role in analyzing and monitoring these vast areas at a global scale. The goal of this study is to demonstrate the potential of intra-annual time series analyses at dense temporal, but coarse spatial resolution for inundation characterization in five river deltas located in four different countries. Based on 250 m MODIS reflectance data we analyze inundation dynamics in four densely populated Asian river deltas—namely the Yellow River Delta (China, the Mekong Delta (Vietnam, the Irrawaddy Delta (Myanmar, and the Ganges-Brahmaputra (Bangladesh, India—as well as one very contrasting delta: the nearly uninhabited polar Mackenzie Delta Region in northwestern Canada for the complete time span of one year (2013. A complex processing chain of water surface derivation on a daily basis allows the generation of intra-annual time series, which indicate inundation duration in each of the deltas. Our analyses depict distinct inundation patterns within each of the deltas, which can be attributed to processes such as overland flooding, irrigation agriculture, aquaculture, or snowmelt and thermokarst processes. Clear differences between mid

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.

Travel Time Estimation on Urban Street Segment

Directory of Open Access Journals (Sweden)

Jelena Kajalić

2018-02-01

Full Text Available Level of service (LOS is used as the main indicator of transport quality on urban roads and it is estimated based on the travel speed. The main objective of this study is to determine which of the existing models for travel speed calculation is most suitable for local conditions. The study uses actual data gathered in travel time survey on urban streets, recorded by applying second by second GPS data. The survey is limited to traffic flow in saturated conditions. The RMSE method (Root Mean Square Error is used for research results comparison with relevant models: Akcelik, HCM (Highway Capacity Manual, Singapore model and modified BPR (the Bureau of Public Roads function (Dowling - Skabardonis. The lowest deviation in local conditions for urban streets with standardized intersection distance (400-500 m is demonstrated by Akcelik model. However, for streets with lower signal density (<1 signal/km the correlation between speed and degree of saturation is best presented by HCM and Singapore model. According to test results, Akcelik model was adopted for travel speed estimation which can be the basis for determining the level of service in urban streets with standardized intersection distance and coordinated signal timing under local conditions.

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

Science.gov (United States)

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

2013-12-01

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

Climatology and time series of surface meteorology in Ny-Ålesund, Svalbard

Directory of Open Access Journals (Sweden)

M. Maturilli

2013-04-01

Full Text Available A consistent meteorological dataset of the Arctic site Ny-Ålesund (11.9° E, 78.9° N spanning the 18 yr-period 1 August 1993 to 31 July 2011 is presented. Instrumentation and data handling of temperature, humidity, wind and pressure measurements are described in detail. Monthly mean values are shown for all years to illustrate the interannual variability of the different parameters. Climatological mean values are given for temperature, humidity and pressure. From the climatological dataset, we also present the time series of annual mean temperature and humidity, revealing a temperature increase of +1.35 K per decade and an increase in water vapor mixing ratio of +0.22 g kg−1 per decade for the given time period, respectively. With the continuation of the presented measurements, the Ny-Ålesund high resolution time series will provide a reliable source to monitor Arctic change and retrieve trends in the future. The relevant data are provided in high temporal resolution as averages over 5 (1 min before (after 14 July 1998, respectively, placed on the PANGAEA repository (doi:10.1594/PANGAEA.793046. While 6 hourly synoptic observations in Ny-Ålesund by the Norwegian Meteorological Institute reach back to 1974 (Førland et al., 2011, the meteorological data presented here cover a shorter time period, but their high temporal resolution will be of value for atmospheric process studies on shorter time scales.

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

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-09-01

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

Clinical time series prediction: towards a hierarchical dynamical system framework

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2014-01-01

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

Turbulencelike Behavior of Seismic Time Series

International Nuclear Information System (INIS)

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

2009-01-01

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

Characterizing time series: when Granger causality triggers complex networks

Science.gov (United States)

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

Urbanization and Sub-urbanization Processes Over Time and Space

International Nuclear Information System (INIS)

Obudho, R.A.

1999-01-01

Until recently, it was thought that Kenya would be an overwhelmingly rural country and that urbanization would not be a problem, because it was associated with modernization and industrialization. Both Government of Kenya (GoK) and international donor agencies fostered rural developmental and agricultural-based strategies without paying attention to rapid rates of urbanization. Today, the high rate of urbanization in Kenya has been added to the long list of potentially devastating development problems that must be addressed. The fundamental problem is that, the urban population is growing very fast while the economic growth and development transformations necessary to support it enhance the quality of urban life are not occurring as rapidly. The new planning strategy for Kenya is to move beyond isolated projects, that emphasize shelter and residential infrastructure towards integrated urban-wide effort that promote urban productivity and reduce constraints on efficiency

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

Science.gov (United States)

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

2017-11-01

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

Neural network versus classical time series forecasting models

Science.gov (United States)

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

2017-05-01

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

Nonlinear time series analysis of the human electrocardiogram

International Nuclear Information System (INIS)

Perc, Matjaz

2005-01-01

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

Vehicle speed prediction via a sliding-window time series analysis and an evolutionary least learning machine: A case study on San Francisco urban roads

Directory of Open Access Journals (Sweden)

Ladan Mozaffari

2015-06-01

Full Text Available The main goal of the current study is to take advantage of advanced numerical and intelligent tools to predict the speed of a vehicle using time series. It is clear that the uncertainty caused by temporal behavior of the driver as well as various external disturbances on the road will affect the vehicle speed, and thus, the vehicle power demands. The prediction of upcoming power demands can be employed by the vehicle powertrain control systems to improve significantly the fuel economy and emission performance. Therefore, it is important to systems design engineers and automotive industrialists to develop efficient numerical tools to overcome the risk of unpredictability associated with the vehicle speed profile on roads. In this study, the authors propose an intelligent tool called evolutionary least learning machine (E-LLM to forecast the vehicle speed sequence. To have a practical evaluation regarding the efficacy of E-LLM, the authors use the driving data collected on the San Francisco urban roads by a private Honda Insight vehicle. The concept of sliding window time series (SWTS analysis is used to prepare the database for the speed forecasting process. To evaluate the performance of the proposed technique, a number of well-known approaches, such as auto regressive (AR method, back-propagation neural network (BPNN, evolutionary extreme learning machine (E-ELM, extreme learning machine (ELM, and radial basis function neural network (RBFNN, are considered. The performances of the rival methods are then compared in terms of the mean square error (MSE, root mean square error (RMSE, mean absolute percentage error (MAPE, median absolute percentage error (MDAPE, and absolute fraction of variances (R2 metrics. Through an exhaustive comparative study, the authors observed that E-LLM is a powerful tool for predicting the vehicle speed profiles. The outcomes of the current study can be of use for the engineers of automotive industry who have been

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

Science.gov (United States)

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

2014-11-01

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

Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

Zucchini, Walter

2009-01-01

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

Constructing ordinal partition transition networks from multivariate time series.

Science.gov (United States)

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

2017-08-10

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

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

Science.gov (United States)

Little, Douglas J; Kane, Deb M

2016-08-01

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

Multiresolution analysis of Bursa Malaysia KLCI time series

Science.gov (United States)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

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

Modelling bursty time series

International Nuclear Information System (INIS)

Vajna, Szabolcs; Kertész, János; Tóth, Bálint

2013-01-01

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

Analysis of Global Urban Temperature Trends and Urbanization Impacts

Science.gov (United States)

Lee, K. I.; Ryu, J.; Jeon, S. W.

2018-04-01

Due to urbanization, urban areas are shrinking green spaces and increasing concrete, asphalt pavement. So urban climates are different from non-urban areas. In addition, long-term macroscopic studies of urban climate change are becoming more important as global urbanization affects global warming. To do this, it is necessary to analyze the effect of urbanization on the temporal change in urban temperature with the same temperature data and standards for urban areas around the world. In this study, time series analysis was performed with the maximum, minimum, mean and standard values of surface temperature during the from 1980 to 2010 and analyzed the effect of urbanization through linear regression analysis with variables (population, night light, NDVI, urban area). As a result, the minimum value of the surface temperature of the urban area reflects an increase by a rate of 0.28K decade-1 over the past 31 years, the maximum value reflects an increase by a rate of 0.372K decade-1, the mean value reflects an increase by a rate of 0.208 decade-1, and the standard deviation reflects a decrease by rate of 0.023K decade-1. And the change of surface temperature in urban areas is affected by urbanization related to land cover such as decrease of greenery and increase of pavement area, but socioeconomic variables are less influential than NDVI in this study. This study are expected to provide an approach to future research and policy-planning for urban temperature change and urbanization impacts.

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...

Analysis of Secular Ground Motions in Istanbul from a Long-Term InSAR Time-Series (1992–2017

Directory of Open Access Journals (Sweden)

Gokhan Aslan

2018-03-01

Full Text Available The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture radar (InSAR time series analysis is a very powerful tool for the operational mapping of ground deformation related to urban subsidence and landslide phenomena. With an analysis spanning almost 25 years of satellite radar observations, we compute an InSAR time series of data from multiple satellites (European Remote Sensing satellites ERS-1 and ERS-2, Envisat, Sentinel-1A, and its twin sensor Sentinel-1B in order to investigate the spatial extent and rate of ground deformation in the megacity of Istanbul. By combining the various multi-track InSAR datasets (291 images in total and analysing persistent scatterers (PS-InSAR, we present mean velocity maps of ground surface displacement in selected areas of Istanbul. We identify several sites along the terrestrial and coastal regions of Istanbul that underwent vertical ground subsidence at varying rates, from 5 ± 1.2 mm/yr to 15 ± 2.1 mm/yr. The results reveal that the most distinctive subsidence patterns are associated with both anthropogenic factors and relatively weak lithologies along the Haramirede valley in particular, where the observed subsidence is up to 10 ± 2 mm/yr. We show that subsidence has been occurring along the Ayamama river stream at a rate of up to 10 ± 1.8 mm/yr since 1992, and has also been slowing down over time following the restoration of the river and stream system. We also identify subsidence at a rate of 8 ± 1.2 mm/yr along the coastal region of Istanbul, which we associate with land reclamation, as well as a very localised subsidence at a rate of 15 ± 2.3 mm/yr starting in 2016 around one of the highest skyscrapers of Istanbul, which was built in 2010.

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.

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.

An Advanced Method to Apply Multiple Rainfall Thresholds for Urban Flood Warnings

Directory of Open Access Journals (Sweden)

Jiun-Huei Jang

2015-11-01

Full Text Available Issuing warning information to the public when rainfall exceeds given thresholds is a simple and widely-used method to minimize flood risk; however, this method lacks sophistication when compared with hydrodynamic simulation. In this study, an advanced methodology is proposed to improve the warning effectiveness of the rainfall threshold method for urban areas through deterministic-stochastic modeling, without sacrificing simplicity and efficiency. With regards to flooding mechanisms, rainfall thresholds of different durations are divided into two groups accounting for flooding caused by drainage overload and disastrous runoff, which help in grading the warning level in terms of emergency and severity when the two are observed together. A flood warning is then classified into four levels distinguished by green, yellow, orange, and red lights in ascending order of priority that indicate the required measures, from standby, flood defense, evacuation to rescue, respectively. The proposed methodology is tested according to 22 historical events in the last 10 years for 252 urbanized townships in Taiwan. The results show satisfactory accuracy in predicting the occurrence and timing of flooding, with a logical warning time series for taking progressive measures. For systems with multiple rainfall thresholds already in place, the methodology can be used to ensure better application of rainfall thresholds in urban flood warnings.

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

Assessing Spatiotemporal Characteristics of Urbanization Dynamics in Southeast Asia Using Time Series of DMSP/OLS Nighttime Light Data

Directory of Open Access Journals (Sweden)

Min Zhao

2018-01-01

Full Text Available Intraregional spatial variations of satellite-derived anthropogenic nighttime light signals are gradually applied to identify different lighting areas with various socioeconomic activity and urbanization levels when characterizing urbanization dynamics. However, most previous partitioning approaches are carried out at local scales, easily leading to multi-standards of the extracted results from local areas, and this inevitably hinders the comparative analysis on the urbanization dynamics of the large region. Therefore, a partitioning approach considering the characteristics of nighttime light signals at both local and regional scales is necessary for studying spatiotemporal characteristics of urbanization dynamics across the large region using nighttime light imagery. Based on the quadratic relationships between the pixel-level nighttime light brightness and the corresponding spatial gradient for individual cities, we here proposed an improved partitioning approach to quickly identify different types of nighttime lighting areas for the entire region of Southeast Asia. Using the calibrated Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS data with greater comparability, continuity, and intra-urban variability, the annual nighttime light imagery spanning years 1992–2013 were divided into four types of nighttime lighting areas: low, medium, high, and extremely high, associated with different intensity of anthropogenic activity. The results suggest that Southeast Asia has experienced a rapid and diverse urbanization process from 1992 to 2013. Areas with moderate or low anthropogenic activity show a faster growth rate for the spatial expansion than the developed areas with intense anthropogenic activity. Transitions between different nighttime lighting types potentially depict the trajectory of urban development, the darker areas are gradually transitioning to areas with higher lighting, indicating conspicuous trends

Perception of open urban space – Bevk square in Nova Gorica

Directory of Open Access Journals (Sweden)

Nataša Bratina

1997-01-01

Full Text Available In research on perception of open urban space, psychological and structural aspects are important, as well as social use. Social use however is most important, because open urban space and its green surfaces satisfies needs of an urban population. The psychological aspect applies to direct experiencing and perception of space, while the structural aspect proves that public urban places are an important category in the urban structure. On the example of Bevk square, Nova Gorica, three types of analyses were carried out. With the structural analysis and evaluation, development and structure of the place were shown, its problems and qualities. The survey on public opinion utilised the method of cognitive mapping, enabled definition of the image of the square, as perceived by everyday users. With the method of time samples events on the square were observed, in different time slots and days of the week.

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.

Performative Urban Design

DEFF Research Database (Denmark)

Performative Urban Design seeks to identify emerging trends in urban design as they are reflected in the city's architecture and spatial design. A “cultural grafting” of the inner city is taking place; architecture and art are playing a crucial, catalytic role in urban development. On the one hand...... these issues through three perspectives: •Sense Architecture; •Place-Making; and •Urban Catalysts. The articles in this volume identify relevant theoretical positions within architecture, art, and urban strategies while demonstrating relevant concepts and methodological approaches drawn from practical......, this development has been rooted in massive investments in “corporate architecture.” On the other, cities themselves have invested heavily in new cultural centers and performative urban spaces that can fulfil the growing desire for entertainment and culture. The anthology Performative Urban Design addresses...

Mapping Global Urban Dynamics from Nighttime Lights - 1992 to 2012

Science.gov (United States)

Xie, Yanhua

Accurate, up-to-date, and consistent information of urban extent is indispensable for numerous applications central to urban planning, ecosystem management, and environmental assessment and monitoring. However, current large-scale urban extent products are not uniform with respect to urban definition, spatial resolution, thematic representation, and temporal frequency. To fill this gap, this study proposed a method to update and backdate global urban extent from currently available urban maps by using nighttime light (NTL) as the main indicator. The method followed three steps: (1) exploring the spatiotemporal variation of NTL thresholds for mapping urban dynamics from NTL time series and developing an object-based thresholding method (i.e., NTL-OUT method, Xie & Weng, 2016b); (2) spatiotemporally enhancing time-series Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL data for detecting broad-scale urban changes (Xie & Weng, 2017); and (3) detecting global urban dynamics during the period between 1992 and 2012 (i.e., 1992, 1997, 2002, 2007, and 2012) from enhanced OLS NTL time series by using the NTL-OUT method. The results show that global urban extent almost doubled during the period from 1992 to 2012, increasing from 0.52 million to 0.98 million km 2, which accounts for 0.39% and 0.72% of the total global land area, respectively. Regionally, the urbanization level varies by continent, with Europe being the most urbanized, followed by North America, Asia, South America, Africa, and Australia-Oceania. In 1992, the urban extent varied from only 0.1% of total continental land area in Australia-Oceania and Africa to 1.18% in Europe. While the proportion of urban extent in North America increased slightly from 1992 to 2002 (i.e., 0.07%), urban extent increased 0.1% for both Asia and South America. In 2012, over 0.7% of the total land was covered by the human built environment, with 0.2% in Africa and Australia-Oceania and around 0

Housing consumption and urbanization

OpenAIRE

Lozano-Gracia, Nancy; Young, Cheryl

2014-01-01

Rapid urbanization in Sub-Saharan Africa places immense pressure on urban services to meet the needs of the burgeoning urban population. Although several country- or city-level reports offer insight into the housing challenges of specific places, little is known about regional patterns affecting housing markets. This lack of clear knowledge on the relative importance of the factors influen...

A real-time control framework for urban water reservoirs operation

Science.gov (United States)

Galelli, S.; Goedbloed, A.; Schwanenberg, D.

2012-04-01

Drinking water demand in urban areas is growing parallel to the worldwide urban population, and it is acquiring an increasing part of the total water consumption. Since the delivery of sufficient water volumes in urban areas represents a difficult logistic and economical problem, different metropolitan areas are evaluating the opportunity of constructing relatively small reservoirs within urban areas. Singapore, for example, is developing the so-called 'Four National Taps Strategies', which detects the maximization of water yields from local, urban catchments as one of the most important water sources. However, the peculiar location of these reservoirs can provide a certain advantage from the logistical point of view, but it can pose serious difficulties in their daily management. Urban catchments are indeed characterized by large impervious areas: this results in a change of the hydrological cycle, with decreased infiltration and groundwater recharge, and increased patterns of surface and river discharges, with higher peak flows, volumes and concentration time. Moreover, the high concentrations of nutrients and sediments characterizing urban discharges can cause further water quality problems. In this critical hydrological context, the effective operation of urban water reservoirs must rely on real-time control techniques, which can exploit hydro-meteorological information available in real-time from hydrological and nowcasting models. This work proposes a novel framework for the real-time control of combined water quality and quantity objectives in urban reservoirs. The core of this framework is a non-linear Model Predictive Control (MPC) scheme, which employs the current state of the system, the future discharges furnished by a predictive model and a further model describing the internal dynamics of the controlled sub-system to determine an optimal control sequence over a finite prediction horizon. The main advantage of this scheme stands in its reduced

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...

Globalization and working time: Work-place hours and flexibility in Germany

NARCIS (Netherlands)

Burgoon, B.; Raess, D.

2007-01-01

This paper examines how economic globalization affects work-place arrangements regulating working time in industrialized countries. Exposure to foreign direct investment and trade can have off-setting effects for work-place bargaining over standard hours and work-time flexibilization, and can be

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.

Rural-urban comparisons of dengue seroprevalence in Malaysia

Directory of Open Access Journals (Sweden)

Cheng Hoon Chew

2016-08-01

Full Text Available Abstract Background Each year an estimated 390 million dengue infections occur worldwide. In Malaysia, dengue is a growing public health concern but estimate of its disease burden remains uncertain. We compared the urban-rural difference of dengue seroprevalence and determined age-specific dengue seroprevalence in Malaysia. Methods We undertook analysis on 11,821 subjects from six seroprevalence surveys conducted in Malaysia between 2001 and 2013, which composed of five urban and two rural series. Results Prevalence of dengue increased with age in both urban and rural locations in Malaysia, which exceeded 90 % among those aged 70 years or beyond. The age-specific rates of the 5 urban surveys overlapped without clear separation among them, while prevalence was lower in younger subjects in rural series than in urban series, the trend reversed in older subjects. There were no differences in the seroprevalence by gender, ethnicity or region. Poisson regression model confirmed the prevalence have not changed in urban areas since 2001 but in rural areas, there was a significant positive time trend such that by year 2008, rural prevalence was as high as in urban areas. Conclusion Dengue seroprevalence has stabilized but persisted at a high level in urban areas since 2001, and is fast stabilizing in rural areas at the same high urban levels by 2008. The cumulative seroprevalence of dengue exceeds 90 % by the age of 70 years, which translates into 16.5 million people or 55 % of the total population in Malaysia, being infected by dengue by 2013.

Rural-urban comparisons of dengue seroprevalence in Malaysia.

Science.gov (United States)

Chew, Cheng Hoon; Woon, Yuan Liang; Amin, Faridah; Adnan, Tassha H; Abdul Wahab, Asmah Hani; Ahmad, Zul Edzhar; Bujang, Mohd Adam; Abdul Hamid, Abdul Muneer; Jamal, Rahman; Chen, Wei Seng; Hor, Chee Peng; Yeap, Lena; Hoo, Ling Ping; Goh, Pik Pin; Lim, Teck Onn

2016-08-18

Each year an estimated 390 million dengue infections occur worldwide. In Malaysia, dengue is a growing public health concern but estimate of its disease burden remains uncertain. We compared the urban-rural difference of dengue seroprevalence and determined age-specific dengue seroprevalence in Malaysia. We undertook analysis on 11,821 subjects from six seroprevalence surveys conducted in Malaysia between 2001 and 2013, which composed of five urban and two rural series. Prevalence of dengue increased with age in both urban and rural locations in Malaysia, which exceeded 90 % among those aged 70 years or beyond. The age-specific rates of the 5 urban surveys overlapped without clear separation among them, while prevalence was lower in younger subjects in rural series than in urban series, the trend reversed in older subjects. There were no differences in the seroprevalence by gender, ethnicity or region. Poisson regression model confirmed the prevalence have not changed in urban areas since 2001 but in rural areas, there was a significant positive time trend such that by year 2008, rural prevalence was as high as in urban areas. Dengue seroprevalence has stabilized but persisted at a high level in urban areas since 2001, and is fast stabilizing in rural areas at the same high urban levels by 2008. The cumulative seroprevalence of dengue exceeds 90 % by the age of 70 years, which translates into 16.5 million people or 55 % of the total population in Malaysia, being infected by dengue by 2013.

Measuring urban sprawl in China by night time light images

Science.gov (United States)

Liu, Lu; Tang, Lin

2017-01-01

In the process of urbanization, a phenomenon called “urban sprawl” usually occurs. This phenomenon may exaggerated the negative effects of urbanization on environment, public and social health, energy efficiency, and maintenance of farmland. Therefore, the understanding of this phenomenon is urgently required for us to achieve sustainable development. This study proposed a group of night time lights (NTL) indicators of urban sprawl, which intend to use the distribution of lightness to quantify urban sprawl. These measures are proved to be efficient in describing urban sprawl. In addition, they are consistent and easy calculating, making comparison analysis easy to be done. These indicators are used to study urban sprawl in China during the year 2000 to 2010, the results show that in the last ten years, metropolitan areas in the northern part of China have undergone a more sprawl-like urban growth compared with other parts of China.

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

The physical environment of positive places: Exploring differences between age groups.

Science.gov (United States)

Laatikainen, Tiina E; Broberg, Anna; Kyttä, Marketta

2017-02-01

Features of the physical environment have an impact on the human behaviour. Thus, planners and policymakers around the world should aim at providing environments that are perceived as being of good quality, in which the residents enjoy spending time and moving around in. It is widely acknowledged that urban environmental quality associates with well-being, but there is currently very little research examining which features of urban environments people of different ages perceive as appealing in their living environments. Individuals experience different age-related developmental environments throughout their life course. Thus, the usage and perceptions of different spaces can also differ between various age groups. Public Participation GIS datasets collected in 2009 and 2011 in Helsinki Metropolitan Area were used to study places perceived as being positive by adults (n=3119) and children (n=672). Participants marked points on a map that were overlaid with GIS data to study whether the physical environment of positive places of different age groups differed. The results demonstrated that the physical environment differs significantly in the positive places of different age groups. The places of adult age groups were characterized by green, blue and commercial spaces, whereas sports, residential and commercial spaces characterize children's and adolescents' places. Older adults' places were found to be closest to home, while adolescents' places were the most distant. Providing appealing environments for all age groups in one setting remains problematic but should nevertheless be strived for, especially in the urban context where a constant competition over different usages of space occurs. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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.

Place-making around high-speed railway stations in China

NARCIS (Netherlands)

Dai, G.

2015-01-01

The rapid expansion of the High-Speed Railway (HSR) network in China generates leapfrog urbanization on the urban periphery in the forms of ambitious blueprint plan around the mega hubs. Nevertheless, most of the station areas and spatial extension lack "place quality". The place-making process of

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...

Jamestown and Disneyland: Two Places in Time.

Science.gov (United States)

Scrofani, E. Robert; Tideman, Robert

This unit for high school students uses two dissimilar places in time; (2) Jamestown, Virginia, founded in 1607, one of the earliest settlements in the United States and (2) Disneyland, California, built in 1956, an institution of contemporary culture. The lessons address two fundamental questions in geography: (1) where? and (2) why here rather…

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.

Toward a 30m resolution time series of historical global urban expansion: exploring variation in North American cities

Science.gov (United States)

Stuhlmacher, M.; Wang, C.; Georgescu, M.; Tellman, B.; Balling, R.; Clinton, N. E.; Collins, L.; Goldblatt, R.; Hanson, G.

2016-12-01

Global representations of modern day urban land use and land cover (LULC) extent are becoming increasingly prevalent. Yet considerable uncertainties in the representation of built environment extent (i.e. global classifications generated from 250m resolution MODIS imagery or the United States' National Land Cover Database) remain because of the lack of a systematic, globally consistent methodological approach. We aim to increase resolution, accuracy, and improve upon past efforts by establishing a data-driven definition of the urban landscape, based on Landsat 5, 7 & 8 imagery and ancillary data sets. Continuous and discrete machine learning classification algorithms have been developed in Google Earth Engine (GEE), a powerful online cloud-based geospatial storage and parallel-computing platform. Additionally, thousands of ground truth points have been selected from high resolution imagery to fill in the previous lack of accurate data to be used for training and validation. We will present preliminary classification and accuracy assessments for select cities in the United States and Mexico. Our approach has direct implications for development of projected urban growth that is grounded on realistic identification of urbanizing hot-spots, with consequences for local to regional scale climate change, energy demand, water stress, human health, urban-ecological interactions, and efforts used to prioritize adaptation and mitigation strategies to offset large-scale climate change. Future work to apply the built-up detection algorithm globally and yearly is underway in a partnership between GEE, University of California in San Diego, and Arizona State University.

A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis

Science.gov (United States)

Zhang, Xiya; Li, Peijun

2018-01-01

Accurate and timely information regarding the extent and spatial distribution of urban areas on regional and global scales is crucially important for both scientific and policy-making communities. Stable nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a unique proxy of human settlement and activity, which has been used in the mapping and analysis of urban areas and urbanization dynamics. However, blooming and saturation effects of DMSP/OLS NTL data are two unresolved problems in regional urban area mapping and analysis. This study proposed a new urban index termed the Temperature and Vegetation Adjusted NTL Urban Index (TVANUI). It is intended to reduce blooming and saturation effects and to enhance urban features by combining DMSP/OLS NTL data with Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra satellite. The proposed index was evaluated in two study areas by comparison with established urban indices. The results demonstrated the proposed TVANUI was effective in enhancing the variation of DMSP/OLS light in urban areas and in reducing blooming and saturation effects, showing better performance than three established urban indices. The TVANUI also significantly outperformed the established urban indices in urban area mapping using both the global-fixed threshold and the local-optimal threshold methods. Thus, the proposed TVANUI provides a useful variable for urban area mapping and analysis on regional scale, as well as for urbanization dynamics using time-series DMSP/OLS and related satellite data.

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 ...

Place and dialect levelling in Denmark

DEFF Research Database (Denmark)

Monka, Malene

This paper demonstrates that processes of globalization such as urbanization and social and geographic mobility may on the one hand lead to dialect leveling and on the other hand to dialect awareness and celebration of linguistic localness (Johnstone 2010). The paper reports on a real time panel......) – but also to place effects, i.e. the ensemble of sociolinguistic conditions within speech localities (Horvath and Horvath 2001; Britain 2009; Blommaert 2010). This paper examines the impact of social and structural factors of place (historic, demographic, and socio-economic) (e.g. Britain 2002) as well...... Gruyter Mouton: 632-648. Trudgill, P. (1974). "Linguistic change and diffusion: description and explanation in sociolinguistic dialect geography." Language in Society 3: 215-246....

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.

City and Urbanity in the Social Discourse

Science.gov (United States)

Dymnicka, Małgorzata; Badach, Joanna

2017-10-01

The aim of our article is an attempt to present the concept of urbanity that has been shaped throughout centuries along with the development of European civilisation and now entered a new phase of social production of space based on cultural dimensions. The future of the majority of World’s population is connected currently with the urban life with the assumption that qualitative characteristics of life in the 21st century define the quality of civilisation itself. Contrary to many scientists’ predictions of the decline of the city and urbanity, new reviving urban projects, social local activities and everyday urbanism appear which are connected with redefinition of the city as a community. The rebirth of cities, currently referred to as “urban renaissance”, “urban resurgence” or “urban revival”, can be also defined in terms of new urbanity regarded as an insightful and creative attitude towards the city and its culture. The elementary order of things was determined in the last decades not by the space but by the time and its acceleration and simultaneously the role of architecture alters. The course of thinking about the city is changing from a single space-time city towards a personalised city, based on individual identities and corresponding places in the physical and virtual space. That can mean a new role of the city in the creation of urbanity. In the era of advanced communication technologies, a question arises about the ontological status of the city when the emphasis is placed on independence and individuality in interactions between people. Social life becomes detached from traditional spatial patterns and practices. We are interested in the urbanity understood in the wider context of cultural urban studies which are focused on new ways of organising the communication space and social relations. We will refer in this article to the values constitutive for the city and urbanity that guided the idea of the city since the dawn of time as well

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 influence of place attachment and experience use history on perceived depreciative visitor behavior and crowding in an urban national park.

Science.gov (United States)

Eder, Renate; Arnberger, Arne

2012-10-01

Research on recreational place attachment suggests that place identity, or the emotional/symbolic ties people have to places, and place dependence, which describes a functional attachment to a specific place, influence the perception of social and environmental site conditions. Recent research, however, has found that place attachment is not always a predictor of such perceptions. This study investigated the influence of place attachment and experience use history on the perception of depreciative visitor behavior, recreation impacts and crowding in an urban national park. In 2006, 605 on-site visitors to the heavily-used Viennese part of the Danube Floodplains National Park were asked about past experience, place attachment, perceptions of depreciative visitor behavior, crowding, changes in visitor numbers during the past ten years, and recreation impacts on wildlife. Confirmatory factor analysis confirmed the two dimensions of place attachment. Linear regression analyses found that place identity and place dependence were related to some perceived depreciative visitor behaviors and visitor number changes but not to crowding, while experience use history additionally related to perceived crowding. Visitors with higher place attachment and past experience were more sensitive to social and environmental site conditions. Management implications of the findings are discussed.

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...

Valuing travel time variability: Characteristics of the travel time distribution on an urban road

DEFF Research Database (Denmark)

Fosgerau, Mogens; Fukuda, Daisuke

2012-01-01

This paper provides a detailed empirical investigation of the distribution of travel times on an urban road for valuation of travel time variability. Our investigation is premised on the use of a theoretical model with a number of desirable properties. The definition of the value of travel time...... variability depends on certain properties of the distribution of random travel times that require empirical verification. Applying a range of nonparametric statistical techniques to data giving minute-by-minute travel times for a congested urban road over a period of five months, we show that the standardized...... travel time is roughly independent of the time of day as required by the theory. Except for the extreme right tail, a stable distribution seems to fit the data well. The travel time distributions on consecutive links seem to share a common stability parameter such that the travel time distribution...

Planning Urban Development from an Outsider’s Perspective: Siem Reap, the Backdrop of Changing Urban Representations

Directory of Open Access Journals (Sweden)

Adele Esposito

2014-11-01

Full Text Available This article explores the internationalization of urban planning in Siem Reap, the town situated as the gateway to the Archaeological Park of Angkor. After Angkor was listed as a World Heritage Site in 1992, international donors and consultants have been involved in the management of Siem Reap Province, where the archaeological park is located. Not only have they been engaged in the conservation and the enhancement of the  archaeological heritage, but they have also planned the future development of nearby Siem Reap. Foreign consultants, coming from Europe and East Asia, have tried to determine what the best suitable models and tools for the urban development of Siem Reap should be, while tourism development and foreign investments were constantly growing. No planning proposal implemented has been completely successful but, several teams of international consultants have carried out new plans that acknowledged the evolution of the urban context. In this article, I question the representation of urban space formulated by these plans and the way they were constructed by consultants coming from different cultural backgrounds and having specific objectives. The article describes how Siem Reap’s built heritage and recent urban phenomena are perceived and analyzes how internationally shared notions and principles (e.g., the discourse of “sustainable development” influence the imagination of future urban development. Faced with the failure of this series of plans, Siem Reap appears to be the backdrop to where evolving urban imagination takes place.

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.

Advanced radar-interpretation of InSAR time series for mapping and characterization of geological processes

Directory of Open Access Journals (Sweden)

F. Cigna

2011-03-01

Full Text Available We present a new post-processing methodology for the analysis of InSAR (Synthetic Aperture Radar Interferometry multi-temporal measures, based on the temporal under-sampling of displacement time series, the identification of potential changes occurring during the monitoring period and, eventually, the classification of different deformation behaviours. The potentials of this approach for the analysis of geological processes were tested on the case study of Naro (Italy, specifically selected due to its geological setting and related ground instability of unknown causes that occurred in February 2005. The time series analysis of past (ERS1/2 descending data; 1992–2000 and current (RADARSAT-1 ascending data; 2003–2007 ground movements highlighted significant displacement rates (up to 6 mm yr⁻¹ in 2003–2007, followed by a post-event stabilization. The deformational behaviours of instable areas involved in the 2005 event were also detected, clarifying typology and kinematics of ground instability. The urban sectors affected and unaffected by the event were finally mapped, consequently re-defining and enlarging the influenced area previously detected by field observations. Through the integration of InSAR data and conventional field surveys (i.e. geological, geomorphologic and geostructural campaigns, the causes of instability were finally attributed to tectonics.

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 ...

Place branding and nonstandard regionalization in Europe

NARCIS (Netherlands)

Boisen, Martin

2015-01-01

Place branding might, could, and maybe even should play a central role in urban and regional governance. The vantage point of this chapter is that every place is a brand and that the processes of nonstandard regionalization that can be witnessed all over Europe create new places and, thus, new place

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

Distribution of radionuclides in urban areas and their removal

International Nuclear Information System (INIS)

Roed, J.; Andersson, K.G.; Garger, E.; Sobotovitch, E.; Matveenko, I.I.

1996-01-01

The major contamination processes in the urban environment are wet and dry deposition with the former leading to much greater deposition per unit of time. Typical deposition patterns for radiocesium in urban areas have been identified for these processes and recent in situ measurements have been used to verify these relations and to investigate the urban weathering effect over long periods. The results of a recent series of field trials of decontamination methods in urban or suburban Russian areas are reported, and this experience has been incorporated in an example of formation of strategies for clean-up in an urban contamination scenario