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On the Error of Prediction of a Time Series
Biometrika, 1972Abstract : Parametric and nonparametric procedures for the prediction of a time series are discussed. In each case the increase in the mean squared error of prediction over its minimum level due to the use of estimated spectra is assessed. The fitting of simple parametric models as approximations is also discussed. (Author)
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Physics Letters A, 1995
Abstract We introduce a technique to characterize and measure predictability in time series. The technique allows one to formulate precisely a notion of the predictable component of given time series. We illustrate our method for both numerical and experimental time series data.
Liming W. Salvino +3 more
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Abstract We introduce a technique to characterize and measure predictability in time series. The technique allows one to formulate precisely a notion of the predictable component of given time series. We illustrate our method for both numerical and experimental time series data.
Liming W. Salvino +3 more
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Optimizations in time series clustering and prediction
Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies, 2010In this paper a combination of time series clustering and prediction is considered. Both clustering and prediction are done by neural networks with supervised and unsupervised learning respectively. Some optimizations of the clustering procedure are proposed for software implementation.
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Forecasting models for prediction in time series
Artificial Intelligence Review, 2011This paper presents the study of three forecasting models—a multilayer percep- tron, a support vector machine, and a hierarchical model. The hierarchical model is made up of a self-organizing map and a support vector machine—the latter on top of the former. The models are trained and assessed on a time series of a Brazilian stock market fund.
Otávio Augusto Salgado Carpinteiro +3 more
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2002
The problem of the prediction of time series belongs to the most important problems of the statistical inference of time series. There are many approaches to these problems, possibly the best known is that based on the Box-Jenkins methodology of modeling time series by using ARMA and ARIMA models, another approach is based on modeling time series by ...
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The problem of the prediction of time series belongs to the most important problems of the statistical inference of time series. There are many approaches to these problems, possibly the best known is that based on the Box-Jenkins methodology of modeling time series by using ARMA and ARIMA models, another approach is based on modeling time series by ...
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Local Prediction In Musical Time Series
International Journal of Modelling and Simulation, 1996Applications in modern nonlinear data analysis techniques indicate that chaotic dynamics are quite common, and that in many cases random behaviour is due to low dimensional chaos rather than complicated dynamics involving many irreducible degrees of freedom. The great promise of chaos lies in the hope that randomness might become predictable.
Mauro Morando +2 more
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1983
The covariance matrix of a stacked data vector (y1′, y2′, ... yn′)′ of a meanzero weakly stationary process {yt} has a special structure: A submatrix Λ0 =Ey1y1′ is located along the main diagonal, the matrix \({\Lambda _\ell } = E{y_{\ell + 1}}{y'_1}\) along the \( \ell\)-th diagonal below the main diagonal, and \({\Lambda _{ - \ell }} = E{y_1}{y'_ ...
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The covariance matrix of a stacked data vector (y1′, y2′, ... yn′)′ of a meanzero weakly stationary process {yt} has a special structure: A submatrix Λ0 =Ey1y1′ is located along the main diagonal, the matrix \({\Lambda _\ell } = E{y_{\ell + 1}}{y'_1}\) along the \( \ell\)-th diagonal below the main diagonal, and \({\Lambda _{ - \ell }} = E{y_1}{y'_ ...
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Incomplete electrocardiogram time series prediction
2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2016The prevalence of Big Data has led to the operation practice based on time series data from multiple sources in many practical applications. The prediction analysis of time series, a fundamental objective of time series data crunch, is an integral part for planning and decision making.
Weiwei Shi 0002 +6 more
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EPNet for chaotic time-series prediction
1997EPNet is an evolutionary system for automatic design of artificial neural networks (ANNs) [1, 2, 3]. Unlike most previous methods on evolving ANNs, EPNet puts its emphasis on evolving ANN'S behaviours rather than circuitry. The parsimony of evolved ANNs is encouraged by the sequential application of architectural mutations.
Xin Yao 0001, Yong Liu 0012
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Adaptive Modularity and Time-Series Prediction
1999This paper focuses on the use of recorded time-series to estimate future values as a function of their past values. We study the local events in input space and apply them as classes of similar patterns to the problem of short-term prediction. The decomposition of the time-series into the patterns formed from d past values denoted as an input vector ...
Mira Trebar, Andrej Dobnikar
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