A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Data Mining Techniques for Associations, Clustering and Classification
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
ScaleNet-multiscale neural-network architecture for time series prediction
IEEE Transactions on Neural Networks
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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.