The Study of Patterns Searching in Time Series Databases

• Main Authors: Ming-shiou Cheng, 鄭明修
• Other Authors: Chien-chiao Yang
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/u8q636

碩士 === 國立臺灣科技大學 === 自動化及控制研究所 === 94 === Abstract Searching for similar patterns in time series, using metrics of similarity matching and collocating searching strategies to find out patterns in time series, is an important issue of researches in data mining. This thesis propose a method which use linear regression to be the metric of similarity for similar patterns searching in time series. Our method not only deals with transformations such as amplitude scaling, amplitude shifting, and time scaling, but also enhances the shortcoming of correlation of correlations(C.C.).In order to promote the efficiency of execution, the algorithm of our method reduces a lot of unnecessary operations during the process of patterns searching. According to the experimental results, our method is faster than C.C. and dynamic time warping(DTW), especially much faster than DTW. In the aspect of accuracy, the searching results of our method matches C.C.’s entirely; moreover, it matches about eighty percent of DTW’s. keywords：Data Mining、Time Series、Patterns、Similarity