Using Dynamic Time Warping to Find Patterns in Time Series

Knowledge discovery in databases presents many interesting challenges within the ¢onte~t of providing computer tools for exploring large data archives. Electronic data .repositories are growing qulckiy and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detect£ug patterns in such data streams or time series is an important knowledge discovery task. This paper describes some pr~|~m;~,ry experiments with a dynamic prograrnm~,~g approach to the problem. The pattern detection algorithm is based on the dynamic time warping technique used in the speech recognition field.