Results for 'multivariate chaotic time series'

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  1.  52
    Prediction of multivariate chaotic time series via radial basis function neural network.Diyi Chen & Wenting Han - 2013 - Complexity 18 (4):55-66.
  2.  3
    Predictive Analysis of Economic Chaotic Time Series Based on Chaotic Genetics Combined with Fuzzy Decision Algorithm.Xiuge Tan - 2021 - Complexity 2021:1-12.
    The irreversibility in time, the multicausality on lines, and the uncertainty of feedbacks make economic systems and the predictions of economic chaotic time series possess the characteristics of high dimensionalities, multiconstraints, and complex nonlinearities. Based on genetic algorithm and fuzzy rules, the chaotic genetics combined with fuzzy decision-making can use simple, fast, and flexible means to complete the goals of automation and intelligence that are difficult to traditional predicting algorithms. Moreover, the new combined method’s ergodicity (...)
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  3.  78
    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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  4.  16
    Analysis of the Time Series Generated by a New High-Dimensional Discrete Chaotic System.Chuanfu Wang, Chunlei Fan, Kai Feng, Xin Huang & Qun Ding - 2018 - Complexity 2018:1-11.
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  5. Biomedical Signal Processing--Time Series Analysis-The Use of Multivariate Autoregressive Modelling for Analyzing Dynamical Physiological Responses of Individual Critically Ill Patients.Kristien Van Aerts Loon, Geert Berghe Meyfroidt & Daniel Berckmans - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 285-297.
  6.  7
    A statistical approach for segregating cognitive task stages from multivariate fmri bold time series.Charmaine Demanuele, Florian Bähner, Michael M. Plichta, Peter Kirsch, Heike Tost, Andreas Meyer-Lindenberg & Daniel Durstewitz - 2015 - Frontiers in Human Neuroscience 9.
  7.  6
    Chaotic Signal Denoising Based on Adaptive Smoothing Multiscale Morphological Filtering.Guiji Tang, Xiaoli Yan & Xiaolong Wang - 2020 - Complexity 2020:1-14.
    Nonlinear time series denoising is the prerequisite for extracting effective information from observation sequence. An effective chaotic signal denoising method not only has a good signal-to-noise ratio enhancement performance, but also can remain as a good unpredictable denoised signal. However, the inherent characteristics of chaos, such as extreme sensitivity to initial values and broadband spectrum, pose challenges for noise reduction of polluted chaotic signals. To address these issues, an adaptive smoothing multiscale morphological filtering is proposed to (...)
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  8.  41
    On Chaotic Consistent Expectations Equilibria.Jochen A. Jungeilges - 2007 - Analyse & Kritik 29 (2):269-289.
    The notion of consistent expectations equilibria as propagated by Hommes/Sorger is reviewed. Focusing on their example of a chaotic CEE constructed in the context of a cobweb model, it is argued that such an equilibrium is a temporary one. Assuming that an agent—modeled as an individual, versatile in applying the basic tools of linear time-series econometrics—has learned the CEE, I analyze the duration of the time period over which the agent maintains her/his beliefs concerning the perceived (...)
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  9.  5
    The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application.Tongfei Lao, Xiaoting Chen & Jianian Zhu - 2021 - Complexity 2021:1-13.
    As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also (...)
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  10.  3
    An Efficient Conformable Fractional Chaotic Map-Based Online/Offline IBSS Scheme for Provable Security in ROM.Chandrashekhar Meshram, Rabha W. Ibrahim & Rafida M. Elobaid - 2022 - Complexity 2022:1-11.
    Chaos distributes with a covert method to condense the dynamic of complexity and satisfies the security requirements of a cryptographic system. This study gives an ability online/offline ID-based short signature scheme using conformable fractional chaotic maps. Furthermore, we establish its security under IBSS existential unforgeability of identity-based short signature under chosen message attack in the random oracle model. Some of the stimulating preparations of obtainable processes are that they give a multiperiod application of the offline storage, which licenses the (...)
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  11.  2
    Bifurcation Analysis and Synchronous Patterns between Field Coupled Neurons with Time Delay.Li Zhang, Xinlei An, Jiangang Zhang & Qianqian Shi - 2022 - Complexity 2022:1-19.
    Neurons encode and transmit signals through chemical synaptic or electrical synaptic connections in the actual nervous system. Exploring the biophysical properties of coupling channels is of great significance for further understanding the rhythm transitions of neural network electrical activity patterns and preventing neurological diseases. From the perspective of biophysics, the activation of magnetic field coupling is the result of the continuous release and propagation of intracellular and extracellular ions, which is very similar to the activation of chemical synaptic coupling through (...)
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  12.  29
    Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation.Juan Ródenas, Manuel García, Raúl Alcaraz & José J. Rieta - 2017 - Complexity:1-13.
    Atrial fibrillation is the most common cardiac arrhythmia in clinical practice. It often starts with asymptomatic and short episodes, which are difficult to detect without the assistance of automatic monitoring tools. The vast majority of methods proposed for this purpose are based on quantifying the irregular ventricular response during the arrhythmia. However, although AF totally alters the atrial activity reflected on the electrocardiogram, replacing stable P-waves by chaotic and time-variant fibrillatory waves, this information has still not been explored (...)
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  13.  1
    A comparison of time series lags and non-lags in Spanish electricity price forecasting using data science models.Belén Vega-Márquez, Javier Solís-García, Isabel A. Nepomuceno-Chamorro & Cristina Rubio-Escudero - forthcoming - Logic Journal of the IGPL.
    Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over four (...)
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  14.  12
    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.  81
    Time Series and Non-reductive Physicalism.Matias Kimi Slavov - 2019 - KronoScope: Journal for the Study of Time 19 (1):25-38.
    McTaggart famously introduced the A- and B-series as rival metaphysical accounts of time. This paper shall reorient the debate over the original distinction. Instead of treating the series as competing theories about the nature of time, it will be argued that they are different viewpoints on a world that is fundamentally physical. To that end, non-reductive physicalism is proposed to reconcile the series.
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  16.  92
    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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  17.  23
    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.
  18.  6
    Time series analysis of discourse: A case study of metaphor in psychotherapy sessions.Dennis Tay - 2017 - Discourse Studies 19 (6):694-710.
    Time series analysis is a technique to describe the structure and forecast values of a particular variable based on a series of sequential observations. While commonly used in finance and engineering to understand structural changes across time, its applicability to humanistic processes like discourse is less clear. This article demonstrates the feasibility and complementary use of TSA with a case study of metaphor use in psychotherapy sessions. A conceptual sketch of how TSA components relate to discourse (...)
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  19.  6
    Time Series Analysis in Forecasting Mental Addition and Summation Performance.Anmar Abdul-Rahman - 2020 - Frontiers in Psychology 11.
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  20.  88
    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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  21. Wind speed forecasting using time series methods : a case study.Sarita Sheoran, Ritik Bavdekar, Sumanta Pasar & Rakhee Kulshrestha - 2022 - In Bhagwati Prasad Chamola, Pato Kumari & Lakhveer Kaur (eds.), Emerging advancements in mathematical sciences. New York: Nova Science Publishers.
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  22. Wind speed forecasting using time series methods : a case study.Sarita Sheoran, Ritik Bavdekar, Sumanta Pasar & Rakhee Kulshrestha - 2022 - In Bhagwati Prasad Chamola, Pato Kumari & Lakhveer Kaur (eds.), Emerging advancements in mathematical sciences. New York: Nova Science Publishers.
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  23. Cointegration: Bayesian Significance Test Communications in Statistics.Julio Michael Stern, Marcio Alves Diniz & Carlos Alberto de Braganca Pereira - 2012 - Communications in Statistics 41 (19):3562-3574.
    To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that “the topic of selecting the cointegrating rank has not yet given very useful (...)
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  24.  21
    Time series analysis for psychological research: examining and forecasting change.Andrew T. Jebb, Louis Tay, Wei Wang & Qiming Huang - 2015 - Frontiers in Psychology 6.
  25.  5
    Time series forecasting with model selection applied to anomaly detection in network traffic.Łukasz Saganowski & Tomasz Andrysiak - 2020 - Logic Journal of the IGPL 28 (4):531-545.
    In herein article an attempt of problem solution connected with anomaly detection in network traffic with the use of statistic models with long or short memory dependence was presented. In order to select the proper type of a model, the parameter describing memory on the basis of the Geweke and Porter-Hudak test was estimated. Bearing in mind that the value of statistic model depends directly on quality of data used for its creation, at the initial stage of the suggested method, (...)
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  26.  12
    A Time Series Approach to Random Number Generation: Using Recurrence Quantification Analysis to Capture Executive Behavior.Wouter Oomens, Joseph H. R. Maes, Fred Hasselman & Jos I. M. Egger - 2015 - Frontiers in Human Neuroscience 9.
  27.  4
    Stochastic TimeSeries Analyses Highlight the Day‐To‐Day Dynamics of Lexical Frequencies.Cameron Holdaway & Steven T. Piantadosi - 2022 - Cognitive Science 46 (12):e13215.
    Standard models in quantitative linguistics assume that word usage follows a fixed frequency distribution, often Zipf's law or a close relative. This view, however, does not capture the near daily variations in topics of conversation, nor the short-term dynamics of language change. In order to understand the dynamics of human language use, we present a corpus of daily word frequency variation scraped from online news sources every 20 min for more than 2 years. We construct a simple time-varying model (...)
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  28.  9
    Interpretable time series kernel analytics by pre-image estimation.Thi Phuong Thao Tran, Ahlame Douzal-Chouakria, Saeed Varasteh Yazdi, Paul Honeine & Patrick Gallinari - 2020 - Artificial Intelligence 286:103342.
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  29.  12
    Time-series analysis of response rates: Alcohol effects on variability-contingent operants.Lowell T. Crow & Paul J. McKinley - 1989 - Bulletin of the Psychonomic Society 27 (6):573-575.
  30. Using timeseries design in the assessment of teaching effectiveness.Huann Shyang Lin & Frances Lawrenz - 1999 - Science Education 83 (4):409-422.
     
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  31.  41
    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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  32.  1
    Phenomenological Structure for the Large Deviation Principle in Time-Series Statistics: A method to control the rare events in non-equilibrium systems.Takahiro Nemoto - 2016 - Singapore: Imprint: Springer.
    This thesis describes a method to control rare events in non-equilibrium systems by applying physical forces to those systems but without relying on numerical simulation techniques, such as copying rare events. In order to study this method, the book draws on the mathematical structure of equilibrium statistical mechanics, which connects large deviation functions with experimentally measureable thermodynamic functions. Referring to this specific structure as the "phenomenological structure for the large deviation principle", the author subsequently extends it to time-series (...)
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  33.  8
    Streaming big time series forecasting based on nearest similar patterns with application to energy consumption.P. Jiménez-Herrera, L. Melgar-GarcÍa, G. Asencio-Cortés & A. Troncoso - 2023 - Logic Journal of the IGPL 31 (2):255-270.
    This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbours algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbours algorithm. The online phase is to predict big time series in real time. In the offline phase, data are (...)
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  34.  32
    Multiscaling comparative analysis of time series and geophysical phenomena.Nicola Scafetta & Bruce J. West - 2005 - Complexity 10 (4):51-56.
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  35.  2
    Analysis on Effectiveness of Surrogate Data-Based Laser Chaos Decision Maker.Norihiro Okada, Mikio Hasegawa, Nicolas Chauvet, Aohan Li & Makoto Naruse - 2021 - Complexity 2021:1-9.
    The laser chaos decision maker has been demonstrated to enable ultra-high-speed solutions of multiarmed bandit problems or decision-making in the GHz order. However, the underlying mechanisms are not well understood. In this paper, we analyze the chaotic dynamics inherent in experimentally observed laser chaos time series via surrogate data and further accelerate the decision-making performance via parameter optimization. We first evaluate the negative autocorrelation in a chaotic time series and its impact on decision-making detail. (...)
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  36.  12
    Finding Structure in Time: Visualizing and Analyzing Behavioral Time Series.Tian Linger Xu, Kaya de Barbaro, Drew H. Abney & Ralf F. A. Cox - 2020 - Frontiers in Psychology 11:521451.
    The temporal structure of behavior contains a rich source of information about its dynamic organization, origins, and development. Today, advances in sensing and data storage allow researchers to collect multiple dimensions of behavioral data at a fine temporal scale both in and out of the laboratory, leading to the curation of massive multimodal corpora of behavior. However, along with these new opportunities come new challenges. Theories are often underspecified as to the exact nature of these unfolding interactions, and psychologists have (...)
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  37.  50
    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.
  38.  2
    A Gaussian Process Latent Variable Model for Subspace Clustering.Shangfang Li - 2021 - Complexity 2021:1-7.
    Effective feature representation is the key to success of machine learning applications. Recently, many feature learning models have been proposed. Among these models, the Gaussian process latent variable model for nonlinear feature learning has received much attention because of its superior performance. However, most of the existing GPLVMs are mainly designed for classification and regression tasks, thus cannot be used in data clustering task. To address this issue and extend the application scope, this paper proposes a novel GPLVM for clustering. (...)
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  39. Memory, non-stationarity, and trend : analysis of environmental time series.Sucharita Ghosh - 2007 - In Felix Kienast, Otto Wildi & S. Ghosh (eds.), A changing world: challenges for landscape research. Dordrecht, The Netherlands: Springer.
     
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  40.  7
    Valence, sensations and appraisals co-occurring with feeling moved: evidence on kama muta theory from intra-individually cross-correlated time series.Anders K. Herting & Thomas W. Schubert - 2022 - Cognition and Emotion 36 (6):1149-1165.
    Emotional experiences typically labelled “being moved” or “feeling touched” may belong to one universal emotion. This emotion, which has been labelled “kama muta”, is hypothesised to have a positive valence, be elicited by sudden intensifications of social closeness, and be accompanied by warmth, goosebumps and tears. Initial evidence on correlations among the kama muta components has been collected with self-reports after or during the emotion. Continuous measures during the emotion seem particularly informative, but previous work allows only restricted inferences on (...)
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  41.  48
    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.
  42.  41
    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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  43. Is the time series reversible? The presidential address.W. R. Inge - 1921 - Proceedings of the Aristotelian Society 21:1.
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  44.  7
    Dynamical noise from time series.O. Kocsis & R. Dadii - 1995 - In R. J. Russell, N. Murphy & A. R. Peacocke (eds.), Chaos and Complexity. Vatican Observatory Publications. pp. 201.
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  45.  9
    Grammar-Mediated Time-Series Prediction.A. Brabazon, K. Meagher, E. Carty, M. O'Neill & P. Keenan - 2005 - Journal of Intelligent Systems 14 (2-3):123-142.
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  46.  2
    The Velocity of Information: Human Thinking During Chaotic Times.David P. Perrodin - 2022 - Rowman & Littlefield Publishers.
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  47. 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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  48.  7
    Uncertainty estimation in the forecasting of the 222Rn radiation level time series at the Canfranc Underground Laboratory.Miguel Cárdenas-Montes - 2022 - Logic Journal of the IGPL 30 (2):227-238.
    Nowadays decision making is strongly supported by the high-confident point estimations produced by deep learning algorithms. In many activities, they are sufficient for the decision-making process. However, in some other cases, confidence intervals are required too for an appropriate decision-making process. In this work, a first attempt to generate point estimations with confidence intervals for the $^{222}$Rn radiation level time series at Canfranc Underground Laboratory is presented. To predict the low-radiation periods allows correctly scheduling the unshielded periods for (...)
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  49.  34
    Identifying how COVID-19-related misinformation reacts to the announcement of the UK national lockdown: An interrupted time-series study.Sally Sheard, Roberto Vivancos, Alex Singleton, Henrdramoorthy Maheswaran, Emily Dearden, Andrew Davies, John Tulloch, Patricia Rossini, Andrew Morse, Chris Kypridemos, Frances Darlington Pollock, Darren Charles, Francisco Rowe, Elena Musi & Mark Green - 2021 - Big Data and Society 8 (1).
    COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter (...)
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  50.  33
    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.
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