This study proposes a temporal data mining method to discover qualitative and quantitative patterns in time series databases. The method performs discrete-valued time series (DIS) analysis on time series databases to search for any similarity and periodicity of patterns that are used for knowledge discovery. In our method there are three levels for mining patterns. At the first level, a structural search based on distance measure models is employed to find pattern structures; the second level performs a value-based search on the discovered patterns using a local polynomial analysis; the third level, based on hidden Markov models (HMMs), finds global patterns from a DTS set. As a result, similar and periodic patterns are successfully extracted. We demonstrate our method on the analysis of "Exchange Rate Patterns" between the U.S. dollar and Australian dollar.
|Title of host publication||Computational intelligence in economics and finance|
|Editors||Shu-Heng Chen, Paul P. Wang, Tzu-Wen Kuo|
|Place of Publication||New York|
|Publisher||Springer, Springer Nature|
|Number of pages||26|
|Publication status||Published - 2004|