Abstract：In view of the influence of multi-variable on the chaotic prediction in practical application, a method for phase space reconstruction of multivariate time series is proposed, and a weighted one-rank local chaos forecasting model on multi-variable was established. The equal probability-based maximum joint entropy and the minimum Shannon entropy are introduced to get the delay time and the embedding dimension respectively, realizing the sub-sequence reconstruction to the chaotic prediction model. The nearest neighbor point method is used to determine the neighborhood of the prediction center to avoid false neighbors, and the correlation analysis is used to determine the observed variables. The model was applied to short-term load forecasting, and the temperature time series was introduced as another observation variable by the analysis of the impact of temperature and other factors related to electric load. The experimental results showed that the prediction accuracy was improved compared with the single variable forecasting method.
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2018, Vol. 39 
