Results for 'time series'

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  1.  27
    Applying a Propensity Score‐Based Weighting Model to Interrupted Time Series Data: Improving Causal Inference in Programme Evaluation.Ariel Linden & John L. Adams - 2011 - Journal of Evaluation in Clinical Practice 17 (6):1231-1238.
  2.  34
    Prediction of Multivariate Chaotic Time Series Via Radial Basis Function Neural Network.Diyi Chen & Wenting Han - 2013 - Complexity 18 (4):55-66.
  3.  22
    Novel Method of Identifying Time Series Based on Network Graphs.Ying Li, Hongduo Caö & Yong Tan - 2011 - Complexity 17 (1):13-34.
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  4.  26
    Multiscaling Comparative Analysis of Time Series and Geophysical Phenomena.Nicola Scafetta & Bruce J. West - 2005 - Complexity 10 (4):51-56.
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  5.  43
    Conventional and Advanced Time Series Estimation: Application to the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database, 1993–2006.John L. Moran & Patricia J. Solomon - 2011 - Journal of Evaluation in Clinical Practice 17 (1):45-60.
  6.  24
    Moment-to-Moment Changes in Feeling Moved Match Changes in Closeness, Tears, Goosebumps, and Warmth: Time Series Analyses.Thomas W. Schubert, Janis H. Zickfeld, Beate Seibt & Alan Page Fiske - 2016 - Cognition and Emotion:1-11.
    Feeling moved or touched can be accompanied by tears, goosebumps, and sensations of warmth in the centre of the chest. The experience has been described frequently, but psychological science knows little about it. We propose that labelling one’s feeling as being moved or touched is a component of a social-relational emotion that we term kama muta. We hypothesise that it is caused by appraising an intensification of communal sharing relations. Here, we test this by investigating people’s moment-to-moment reports of feeling (...)
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  7.  5
    Learning Causal Structure From Undersampled Time Series.David Danks & Sergey Plis - unknown
    Even if one can experiment on relevant factors, learning the causal structure of a dynamical system can be quite difficult if the relevant measurement processes occur at a much slower sampling rate than the “true” underlying dynamics. This problem is exacerbated if the degree of mismatch is unknown. This paper gives a formal characterization of this learning problem, and then provides two sets of results. First, we prove a set of theorems characterizing how causal structures change under undersampling. Second, we (...)
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  8.  23
    Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.Petko Kusev, Paul Schaik, Krasimira Tsaneva‐Atanasova, Asgeir Juliusson & Nick Chater - 2018 - Cognitive Science 42 (1):77-102.
    When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping. Events in a time series can be experienced sequentially, or they can also be retrospectively viewed simultaneously, not experienced individually in real time. In one experiment, (...)
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  9.  41
    Legislative Production in Comparative Perspective: Cross-Sectional Study of 42 Countries and Time-Series Analysis of the Japan Case.Kentaro Fukumoto - 2008 - Japanese Journal of Political Science 9 (1):1-19.
    Legislative scholars have debated what factors (e.g. divided government) account for the number of important laws a legislative body passes per year. This paper presents a monopoly model for explaining legislative production. It assumes that a legislature adjusts its law production so as to maximize its utility. The model predicts that socio-economic and political changes increase the marginal benefit of law production, whereas low negotiation costs and ample legislative resources decrease the marginal cost of law production. The model is tested (...)
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  10.  8
    Turn Taking, Team Synchronization, and Non-Stationarity in Physiological Time Series.Stephen J. Guastello, David E. C. Marra, Julian Castro, Michael Equi & Anthony F. Peressini - 2017 - Nonlinear Dynamics, Psychology, and Life Sciences 21:319-334.
    This study investigated the stationarity of electrodermal time series collected in situations where turn taking in human interactions are involved. In this context, the stationarity of the time series is the extent to which a simple model can be used to fit the entire time series. The experiment involved seven participants in an emergency response simulation against one opponent. They generated 48 time series across six simulations, which were split and re-spliced to (...)
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  11.  15
    Impact of Fiscal Deficit on Inflation in Sri Lanka: An Econometric Time Series Analysis.Ahamed Lebbe Mohamed Aslam & S. M. Ahamed Lebbe - 2016 - International Letters of Social and Humanistic Sciences 70:8-13.
    Source: Author: Ahamed Lebbe Mohamed Aslam, S.M. Ahamed Lebbe There is a relationship between the fiscal deficit and inflation, which was confirmed empirically in several studies conducted in many countries. Sri Lanka has been encountering the problem of inflation for the recent years. But in Sri Lanka, this proposition has not yet been studied scientifically. Therefore, this study was going to fill this gap. The objective of this study was to test the impact of fiscal deficit on inflation in Sri (...)
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  12.  9
    The Blessed Virgin and the Two Time-Series: Hervaeus Natalis and Durand of St. Pourçain on Limit Decision.Can Laurens Löwe - 2017 - Vivarium 55 (1-3):36-59.
    This paper examines the accounts of limit decision advanced by Hervaeus Natalis and Durand of St. Pourçain in their respective discussions of the sanctification of the Blessed Virgin. Hervaeus and Durand argue, against Aristotle, that the temporal limits of certain changes, including Mary’s sanctification, should be assigned in discrete rather than continuous time. The paper first considers Hervaeus’ discussion of limit decision and argues that, for Hervaeus, a solution of temporal limits in terms of discrete time can coexist (...)
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  13.  13
    The Importance of Modeling Comorbidity Using an Intra-Individual, Time-Series Approach.Dana Tzur-Bitan, Nachshon Meiran & Golan Shahar - 2010 - Behavioral and Brain Sciences 33 (2-3):172-173.
    We suggest that the network approach to comorbidity (Cramer et al.) is best examined by using longitudinal, multi-measurement, intra-individual data. Employment of time-series analysis to the examination of the generalized anxiety disorder and major depressive disorder comorbidity enables a detailed appreciation of fluctuations and causal trajectories in terms of both symptoms and cognitive vulnerability.
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  14.  8
    Efficient Time Series Clustering and Its Application to Social Network Mining.Qianchuan Zhao & Cangqi Zhou - 2014 - Journal of Intelligent Systems 23 (2):213-229.
    Mining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between (...)
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  15.  15
    The Logomotor Behavior of the Nurse Shark,Ginglymostoma Cirratum; a Time Series Analysis.J. H. Matis, H. Kleerekoper & D. Gruber - 1975 - Acta Biotheoretica 24 (3-4):127-135.
    In an approach to quantify the locomotor response to environmental stimuli in fishes and its central control mechanisms, initially stochastic models of spontaneous locomotor behavior are being formulated. In the present paper, the locomotor patterns of three active nurse shark,Ginglymostoma cirratum, in six experiments are converted into 17 locomotor variables and found to have definite time series structure. Sixty-seven of the 102 first order serial correlation coefficients are statistically significant, the incidence rate of which differs between experiments and (...)
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  16.  9
    Fertility and Suicide Rates: A Time Series Analysis in the United States.David Lester & Bijou Yang - 1992 - Journal of Biosocial Science 24 (1):97-102.
    In a time series study of the USA from 1933 to 1984, fertility rates were associated with the suicide rates of those aged 15–44. The higher the fertility rate the lower the suicide rate for these age groups, for both whites and non-whites, and for both men and women. The results were seen as supporting Durkheim's theory of suicide.
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  17.  74
    On the Plurality of Times: Disunified Time and the A-Series.Ryan Nefdt - 2013 - South African Journal of Philosophy 32 (3):249-260.
    In this paper, I investigate the nature of the metaphysical possibility of disunified time. A possibility that I argue presents unique problems for those who adhere to a strict A-theory of time, particularly those A-theorists who propose a presentist view. The first part of the paper discusses various arguments against the coherence of the concept of disunified time. I attempt to discount each of these objections and show that disunified time is indeed a possible and consistent (...)
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  18.  10
    Moment-to-Moment Changes in Feeling Moved Match Changes in Closeness, Tears, Goosebumps, and Warmth: Time Series Analyses.Thomas W. Schubert, Janis H. Zickfeld, Beate Seibt & Alan Page Fiske - 2018 - Cognition and Emotion 32 (1):174-184.
  19.  9
    Using Machine Learning to Identify Structural Breaks in Single-Group Interrupted Time Series Designs.Ariel Linden & Paul R. Yarnold - 2016 - Journal of Evaluation in Clinical Practice 22 (6):855-859.
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  20.  43
    Nonstationary Time Series, Cointegration, and the Principle of the Common Cause.Kevin D. Hoover - 2003 - British Journal for the Philosophy of Science 54 (4):527-551.
    Elliot Sober ([2001]) forcefully restates his well-known counterexample to Reichenbach's principle of the common cause: bread prices in Britain and sea levels in Venice both rise over time and are, therefore, correlated; yet they are ex hypothesi not causally connected, which violates the principle of the common cause. The counterexample employs nonstationary data—i.e., data with time-dependent population moments. Common measures of statistical association do not generally reflect probabilistic dependence among nonstationary data. I demonstrate the inadequacy of the counterexample (...)
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  21.  12
    A Matching Framework to Improve Causal Inference in Interrupted Time-Series Analysis.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (2):408-415.
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  22.  11
    Combining Synthetic Controls and Interrupted Time Series Analysis to Improve Causal Inference in Program Evaluation.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (2):447-453.
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  23.  2
    Supply and Demand Effects in Television Viewing. A Time Series Analysis.Hans Franses, Rob Eisinga & Maurice Vergeer - 2012 - Communications 37 (1):79-98.
    In this study we analyze daily data on television viewing in the Netherlands. We postulate hypotheses on supply and demand factors that could impact the amount of daily viewing time. Although the general assumption is that supply and demand often correlate, we see that for television this is only marginally the case. Especially diversity of program supply, often deemed very important in media markets, does not affect television viewing behavior. Most variation in television viewing can be attributed to habit (...)
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  24.  8
    Persistent Threats to Validity in Single‐Group Interrupted Time Series Analysis with a Cross Over Design.Ariel Linden - 2017 - Journal of Evaluation in Clinical Practice 23 (2):419-425.
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  25.  19
    Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: An R Package.Moreno I. Coco & Rick Dale - 2014 - Frontiers in Psychology 5.
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  26.  11
    Using Permutation Tests to Enhance Causal Inference in Interrupted Time Series Analysis.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (3):496-501.
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  27.  12
    Using Group-Based Trajectory Modelling to Enhance Causal Inference in Interrupted Time Series Analysis.Ariel Linden - 2018 - Journal of Evaluation in Clinical Practice 24 (3):502-507.
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  28.  11
    Time-Series Analysis of Embodied Interaction: Movement Variability and Complexity Matching As Dyadic Properties.Leonardo Zapata-Fonseca, Dobromir Dotov, Ruben Fossion & Tom Froese - 2016 - Frontiers in Psychology 7.
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  29.  9
    The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series.Manuel Vargas, Guillermo Fuertes, Miguel Alfaro, Gustavo Gatica, Sebastian Gutierrez & María Peralta - 2018 - Complexity 2018:1-13.
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  30.  5
    Calculation of Average Mutual Information and False-Nearest Neighbors for the Estimation of Embedding Parameters of Multidimensional Time Series in Matlab.Sebastian Wallot & Dan Mønster - 2018 - Frontiers in Psychology 9.
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  31.  12
    Multidimensional Recurrence Quantification Analysis for the Analysis of Multidimensional Time-Series: A Software Implementation in MATLAB and Its Application to Group-Level Data in Joint Action.Sebastian Wallot, Andreas Roepstorff & Dan Mønster - 2016 - Frontiers in Psychology 7.
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  32.  4
    Using Machine Learning to Evaluate Treatment Effects in Multiple-Group Interrupted Time Series Analysis.Ariel Linden & Paul R. Yarnold - 2018 - Journal of Evaluation in Clinical Practice 24 (4):740-744.
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  33.  4
    A Dynamical Method to Estimate Gene Regulatory Networks Using Time-Series Data.Chengyi Tu - 2016 - Complexity 21 (2):134-144.
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  34.  3
    Complexity in Neural and Financial Systems: From Time-Series to Networks.Tiziano Squartini, Andrea Gabrielli, Diego Garlaschelli, Tommaso Gili, Angelo Bifone & Fabio Caccioli - 2018 - Complexity 2018:1-2.
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  35.  61
    Time-Series of Ephemeral Impressions: The Abhidharma-Buddhist View of Conscious Experience.Monima Chadha - 2015 - Phenomenology and the Cognitive Sciences 14 (3):543-560.
    In the absence of continuing selves or persons, Buddhist philosophers are under pressure to provide a systematic account of phenomenological and other features of conscious experience. Any such Buddhist account of experience, however, faces further problems because of another cardinal tenet of Buddhist revisionary metaphysics: the doctrine of impermanence, which during the Abhidharma period is transformed into the doctrine of momentariness. Setting aside the problems that plague the Buddhist Abhidharma theory of experience because of lack of persons, I shall focus (...)
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  36.  16
    Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis.Hafiza Mamona Nazir, Ijaz Hussain, Muhammad Faisal, Alaa Mohamd Shoukry, Showkat Gani & Ishfaq Ahmad - 2019 - Complexity 2019:1-14.
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  37.  17
    Estimation of Fixed Points for Nonlinear Time Series.Shapour Mohammadi & Hossein Abbasinezhad - 2014 - Complexity 19 (5):30-39.
  38.  4
    A Non-Parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series.Soheil Keshmiri, Hidenobu Sumioka, Ryuji Yamazaki & Hiroshi Ishiguro - 2017 - Frontiers in Human Neuroscience 11.
  39.  3
    Efficient Computation of Multiscale Entropy Over Short Biomedical Time Series Based on Linear State-Space Models.Luca Faes, Alberto Porta, Michal Javorka & Giandomenico Nollo - 2017 - Complexity 2017:1-13.
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  40.  4
    Recent Improvements on Complexity Measures for Time Series.David Cuesta-Frau, Daniel Abásolo & Daniel Novák - 2019 - Complexity 2019:1-2.
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  41.  6
    What is the Cause of the Decline in Maternal Mortality in India? Evidence From Time Series and Cross-Sectional Analyses.Srinivas Goli & Abdul C. P. Jaleel - 2014 - Journal of Biosocial Science 46 (3):351-365.
  42.  6
    LMC and SDL Complexity Measures: A Tool to Explore Time Series.José Roberto C. Piqueira & Sérgio Henrique Vannucchi Leme de Mattos - 2019 - Complexity 2019:1-8.
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  43.  8
    What is the Cause of the Decline in Maternal Mortality in India? Evidence From Time Series and Cross-Sectional Analyses.Srinivas Goli & Abdul C. P. Jaleel - 2013 - Journal of Biosocial Science 46 (3):1-15.
  44. Convolutional Networks for Images, Speech, and Time Series.Yann LeCun & Yoshua Bengio - 1995 - In Michael A. Arbib (ed.), Handbook of Brain Theory and Neural Networks. MIT Press. pp. 3361.
     
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  45.  7
    Investigation of the Spatial Clustering Properties of Seismic Time Series: A Comparative Study From Shallow to Intermediate-Depth Earthquakes.Ke Ma, Long Guo & Wangheng Liu - 2018 - Complexity 2018:1-10.
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  46.  10
    A Hybrid Grey Wolf Optimiser Algorithm for Solving Time Series Classification Problems.Heba Al Nsour, Mohammed Alweshah, Abdelaziz I. Hammouri, Hussein Al Ofeishat & Seyedali Mirjalili - forthcoming - Journal of Intelligent Systems.
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  47.  17
    Proposal of an Information Compilation Method for Massive News Video Data Based on Their Time-Series Semantic Structure.Ichiro Ide, Tomoyoshi Kinoshita, Tomokazu Takahashi, Hiroshi Mo, Norio Katayama, Shin'ichi Satoh & Hiroshi Murase - 2008 - Transactions of the Japanese Society for Artificial Intelligence 23:282-292.
  48.  67
    Is Symbolic Dynamics the Most Efficient Data Compression Tool for Chaotic Time Series?Alfred Hubler - 2012 - Complexity 17 (3):5-7.
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  49. Orbital Decomposition for Multiple Time Series Comparisons.D. Pincus, D. L. Ortega & A. M. Metten - 2011 - In Stephen J. Guastello & R. A. M. Gregson (eds.), Nonlinear Dynamical Systems Analysis for the Behavioral Sciences Using Real Data. Crc Press.
     
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  50.  8
    Ensembles of Text and Time-Series Models for Automatic Generation of Financial Trading Signals From Social Media Content.Omar A. Bari & Arvin Agah - forthcoming - Journal of Intelligent Systems.
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