关键词: 时间序列, 离散小波变换, 特征提取, 趋势预测

Abstract:

A new method of time series data mining using discrete wavelet transform was proposed. The main property of this method lied in its feature extraction strategy, which was different from other methods before. Instead of using only the first k coefficients or the largest k coefficient, which emphasized on energy preservation, this new method decided extracted coefficients according to their classification ability on real time sequences in the training set. Generally speaking, this method tried to select coefficients from all wavelet coefficients to form a feature coefficient set, which best enlarged the distance between different classes and reduced the distance within same class. With this feature coefficient set, we can make prediction on test sequences set by calculating pertaining degree of each sequence to each class. The prediction result is the pertaining relation with largest pertaining degree. For real time series data used in our research, the efficiency and accuracy of this method is satisfying.

Key words: time series, discrete wavelet transform, feature extraction, trend prediction