Data Mining Based on Segmented Time Warping Distance in Time Series Database

Data mining in time series database is an important task, most research work are based comparing time series with Euclidean distance measure or its transformations. However Euclidean distance measure will change greatly when the compared time series move slightly along the time-axis. It's impossible to get satisfactory result when using Euclidean distance in many cases. Dynamic time warping distance is a good way to deal with these cases, but it's very difficult to compute which limits its application. In this paper, a novel method is proposed to avoid the drawback of Euclidean distance measure. It first divides time series into several line segments based on some feature points which are Chosen by some heuristic method. Each time series is converted into a segmented sequence, and then a new distance measure called feature points segmented time warping distance is defined based this segmentation. Compared with the classical dynamic time warping distance, this new method is much more fast in speed and almost no degrade in accuracy. Finally, implements two completed and detailed experiments to prove its superiority.