| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 92 === According to the 2003 Nobel Economic Prize, some behaviors and rules exist in time series at financial market. However, these behavior and rules can be found not only by traditional statistic or mathematical tools but by pattern recognition methodologies.
The problem of pattern recognition includes image processing, speech recognition, time series data analysis and so on. Image processing and speech recognition have been widely applied to business and entertainment According to the progress of information technology, the methodologies of pattern recognition are enhanced with high-speed computation and high-volume storage devices.
The distance measures in image and speech processing of pattern recognition are necessary and important tools. Distance measures can be applied to clustering and similarity search. Dynamic time warping is one of distance measures, which is used and well-performed at speech recognition. In 1994, DTW was introduced in data mining domain by Berndt and Clifford. Although it performs well than traditional distance measure, such as Euclidean distance, cost of computation can be large because of the algorithm of DTW. This cost can limit the performance while using DTW on real-time analysis.
An improved DTW, trie-structure DTW is proposed in this research. By using hierarchical clustering, trie-structure DTW will be applied to analysis of time series of minute-data in TAIEX(Taiwan Stock Exchange Corporation Capitalization Weighted Stock Index). The classic DTW and Euclidean distance will be compared with trie-structure DTW in this research.
After experiments, using trie-structure DTW would get better performance than E
Euclidean distance measure. Furthermore, the time cost of trie-structure DTW is less than the classic DTW and it’s possible to use the improved DTW on real-time financial prediction.
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