Evolutionary segmentation of financial time series into subsequences

Abstract

Time series data are difficult to manipulate. When they can be transformed into meaningful symbols, it becomes an easy task to query and understand them. While most recent works in time series query only concentrate on how to identify a given pattern from a time series, they do not consider the problem of identifying a suitable set of time points based upon which the time series can be segmented in accordance with a given set of pattern templates, e.g., a set of technical analysis patterns for stock analysis. On the other hand, using fixed length segmentation is only a primitive approach to such kind of problem and hence a dynamic approach is preferred so that the time series can be segmented flexibly and effectively. In view of the fact that such a segmentation problem is actually an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary segmentation algorithm in this paper. Encouraging experimental results in segmenting the Hong Kong Hang Seng Index using 22 technical analysis patterns are reported.