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Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size
Joint Authors
Fu, Huimin
Wang, Zhihua
Zhang, Yongbo
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-09
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size.
Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR) prediction approach with rolling mechanism is proposed.
In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step.
Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set.
This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort.
The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations.
The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.
American Psychological Association (APA)
Wang, Zhihua& Zhang, Yongbo& Fu, Huimin. 2014. Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-481741
Modern Language Association (MLA)
Wang, Zhihua…[et al.]. Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size. Mathematical Problems in Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-481741
American Medical Association (AMA)
Wang, Zhihua& Zhang, Yongbo& Fu, Huimin. Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-481741
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-481741