Novel Algorithm for Time Series Data Mining Based on Dynamic Time Warping

Time series are important kinds of complex data, while a growing attention has been paid to mining time series knowledge recently. Typically Euclidean distance measure and its variation or extensions are used for comparing time series. However, it may be a brittle distance measure because of less robustness. Dynamic time warp (DTW) is a patter matching algorithm based on nonlinear dynamic programming technique. In this paper, we present a similarity searching algorithm based on DTW, which searches matching sub series by computing the minimization of warp path. Experiments about cluster analysis for two different distance measure are implemented on synthetic control chart time series. The results shows that the measure, presented in this paper, has stronger robustness to amplitude scaling, noise and linear drift for time series, so that it has good value for applications.