Name
Abstract | ||
|---|---|---|
Stock data in the form of multiple time series are difficult to process, analyze and mine. However, when they can be transformed into meaningful symbols like technical patterns, it becomes easier. Most recent work on time series queries concentrates only on how to identify a given pattern from a time series. Researchers do not consider the problem of identifying a suitable set of time points for segmenting the time series in accordance with a given set of pattern templates (e.g., a set of technical patterns for stock analysis). On the other hand, using fixed length segmentation is a primitive approach to this problem; hence, a dynamic approach (with high controllability) is preferred so that the time series can be segmented flexibly and effectively according to the needs of users and applications. In view of the fact that such a segmentation problem is an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary time series segmentation algorithm. This approach allows a sizeable set of stock patterns to be generated for mining or query. In addition, defining the similarity between time series (or time series segments) is of fundamental importance in fitness computation. By identifying perceptually important points directly from the time domain, time series segments and templates of different lengths can be compared and intuitive pattern matching can be carried out in an effective and efficient manner. Encouraging experimental results are reported from tests that segment the time series of selected Hong Kong stocks. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICDM.2002.1183889 | ICDM |
Keywords | Field | DocType |
evolutionary time series segmentation,optimisation,time domain,technical patterns,evolutionary time series segmentationalgorithm,multiple time series aredifficult,agiven set,evolutionary computation,recent work ontime series,fitness computation,dynamic approach,financial data processing,perceptually important points,pattern matching,intuitive pattern matching,stock markets,meaningful symbols,stock patterns,optimization,stock data mining,time seriessegments,hong kong stocks,data mining,agiven pattern,multiple time series,time series segmentsand template,time series,pattern templates,suitable set,pattern analysis,evolutionary computing,shape,controllability,time series analysis,testing,optimization problem | Time domain,Data mining,Time series,Time-series segmentation,Market segmentation,Computer science,Segmentation,Evolutionary computation,Artificial intelligence,Pattern matching,Optimization problem,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-1754-4 | 23 | 0.92 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fu Lai Chung | 1 | 1534 | 86.72 |
| Tak-chung Fu | 2 | 407 | 21.29 |
| Robert W. P. Luk | 3 | 554 | 55.57 |
| Vincent T. Y. Ng | 4 | 504 | 122.85 |