Mining for similarities in time series data using wavelet-based feature vectors and neural networks

This paper presents a comparison between different wavelet feature vectors for data mining of nonstationary time series that occurs in an electricity supply network. Three different wavelet algorithms are simulated and applied on nine classes of power signal time series, which primarily belongs to an important problem area called electric power quality. In contrast to the wavelet analysis, the paper presents a new approach called S-transform-based time frequency analysis in processing power quality disturbance data. Certain pertinent feature vectors are extracted using the well-known wavelet methods and the new approach using S-transform. Neural networks are then used to compute the classification accuracy of the feature vectors. Certain characteristics of the wavelet feature vectors are apparent from the results. Further in large data sets partitioning is done and similarities of pattern vectors present in different sections are determined. The approach is a general one and can be applied to pattern classification, similarity determination, and knowledge discovery in time varying data patterns occurring in many practical sciences and engineering problems.