Short-term data forecasting based on wavelet transformation and chaos theory

2017 3rd International Conference on Applied Materials and Manufacturing Technology

Wang, Yi^1 ; Li, Cunbin^1 ; Zhang, Liang^1

Institute School of Economics and Management, North China Electric Power University, Beijing

102206, China^1

关键词: Chaos time series;    Chaotic behaviors;    Forecasting methods;    ITS applications;    Largest Lyapunov exponent;    Short-term data;    Small data set;    Wavelet transformations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/242/1/012121/pdf
DOI  :  10.1088/1757-899X/242/1/012121

来源: IOP

【 摘 要 】

A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of "data nail" on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.