Set Pair Analysis Based on Phase Space Reconstruction Model and Its Application in Forecasting Extreme Temperature
| Autor: | Ling Zhang, Ling-xia Qiao, Wan-ying Ma, Yin Zhang, Xiaohua Yang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Rank (linear algebra)
Artificial neural network Series (mathematics) Article Subject General Mathematics lcsh:Mathematics General Engineering lcsh:QA1-939 Set (abstract data type) Autoregressive model Dimension (vector space) Approximation error lcsh:TA1-2040 Phase space Statistics lcsh:Engineering (General). Civil engineering (General) Algorithm Mathematics |
| Zdroj: | Mathematical Problems in Engineering, Vol 2013 (2013) |
| ISSN: | 1024-123X |
| DOI: | 10.1155/2013/516150 |
| Popis: | In order to improve the precision of forecasting a time series, set pair analysis based on phase space reconstruction (SPA-PSR) model is established. In the new model, by using chaos analysis, we reconstruct the phase space with delay time and embedding dimension. Based on it, we rebuilt history sets and current sets in the SPA-PSR model. Two cases of forecasting extreme temperature in Mount Wutai and Datong are taken to examine the performance of SPA-PSR model. The results indicate that the mean relative error (MRE) of SPA-PSR model has decreased by 65.97%, 59.32%, and 7.79% in the case of Mount Wutai and 29.11%, 32.82%, and 9.03% in the case of Datong, respectively, compared with autoregression (AR) model, rank set pair analysis (R-SPA) model, and Back-Propagation (BP) neural network model. It gives a theoretical support for set pair analysis and improves precision of numerical forecasting. |
| Databáze: | OpenAIRE |
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