Abstract
The business field is one of the important fields where the data mining technology is applied. The study mainly focuses on different attribute object’s quantitative prediction and customer structure’s qualitative prediction. Aiming at the characteristics of time series in business field, such as near-periodicity, non-stationarity and nonlinearity, the wavelet-neural networks-ARMA method is proposed and its application is examined in this paper. The hidden period and the non-stationarity 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. The given example elucidates that the forecasting method mentioned in this paper can be employed to the business field successfully and efficiently.
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Tong, W., Li, Y. (2005). Wavelet Method Combining BP Networks and Time Series ARMA Modeling for Data Mining Forecasting. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_21
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DOI: https://doi.org/10.1007/11539117_21
Publisher Name: Springer, Berlin, Heidelberg
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