Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification ac- curacy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features.