A wavelet analysis based data processing for time series of data mining predicting

Authors:
Weimin Tong;Yijun Li;Qiang Ye
Affiliations:
School of Management, Harbin Institute of Technology, Harbin, China;School of Management, Harbin Institute of Technology, Harbin, China;School of Management, Harbin Institute of Technology, Harbin, China
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

This paper presents wavelet method for time series in business-field forecasting. An autoregressive moving average (ARMA) model is used, it can model the near-periodicity, nonstationarity and nonlinearity existed in business short-term time series. According to the wavelet denoising, wavelet decomposition and wavelet reconstruction, the hidden period and the nonstationarity existed in time series are extracted and separated by wavelet transformation. The characteristic of wavelet decomposition series is applied to BP networks and an autoregressive moving average (ARMA) model. It shows that the proposed method can provide more accurate results than the conventional techniques, like those only using BP networks or autoregressive moving average (ARMA) models.