Time-series similarity measurement method based on segmented statistical approximate representation

The invention discloses a time-series similarity measurement method based on segmented statistical approximate representation. The method comprises the steps of feature extraction and dynamic pattern matching. First, a time series is segmented into sub series, the various statistical features of the sub series are sequentially extracted, and local pattern feature vectors are constructed; then the distance between the local pattern feature vectors is calculated by the weighted Euclidean distance, local pattern matching is achieved, the matched local pattern is used as the sub program of a dynamic programming algorithm, and global pattern matching is achieved. The method is superior to other measurement methods by a large degree on the aspects of measurement precision and calculation efficiency, and plays an important role in daily activities and industrial production of people, for example, financial transactions, traffic control, air quality and temperature monitoring, industrial flow monitoring, medical diagnosis and the like. Large scale sampling data or high-speed dynamic data flow is subjected to similarity-based search, classification, clustering, prediction, abnormal detection, on-line pattern recognition and the like.