Evolutionary Time Series Segmentation for Stock Data Mining

• Authors: Fu-lai Chung;Tak-chung Fu;Robert Luk;Vincent Ng
• Venue: ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
• Year: 2002

Stock data in the form of multiple time series aredifficult to process, analyze and mine. However, when theycan be transformed into meaningful symbols like technicalpatterns, it becomes an easier task. Most recent work ontime series queries only concentrates on how to identify agiven pattern from a time series. Researchers do notconsider the problem of identifying a suitable set of timepoints for segmenting the time series in accordance with agiven set of pattern templates (e.g., a set of technicalpatterns for stock analysis). On the other hand, using fixedlength segmentation is a primitive approach to thisproblem; hence, a dynamic approach (with highcontrollability) is preferred so that the time series can besegmented flexibly and effectively according to the needs ofthe users and the applications. In view of the facts that sucha segmentation problem is an optimization problem andevolutionary computation is an appropriate tool to solve it,we propose an evolutionary time series segmentationalgorithm. This approach allows a sizeable set of stockpatterns to be generated for mining or query. In addition,defining the similarity between time series (or time seriessegments) is of fundamental importance in fitnesscomputation. By identifying the perceptually importantpoints directly from the time domain, time series segmentsand templates of different lengths can be compared andintuitive pattern matching can be carried out in an effectiveand efficient manner. Encouraging experimental results arereported from tests that segment the time series of selectedHong Kong stocks.