A time series similarity searching method based on segmentation weight

Abstract

The invention discloses a time series similarity searching method based on segment weight, which comprises the following steps: (1) segmenting a query sequence q by adopting an important turning pointof the time series; (2) establishing piecewise Euclidean distance with weights as similarity measure of time series; (3) performing k-nearest neighbor similarity querying on the q, searching for thek most similar sequences of the query sequence q; (4) ending the query if the user is satisfied with the query result obtained in the step (3); If the user is not satisfied with the query result obtained in the step (3), marking the result and entering the step (5); (5) allowing the system to update the weights of the segments by using the sequence of user tags and returning to step (2). The invention automatically updates the weights through user feedback, adaptively learns the attention degree of the user to different segments, can improve the accuracy of similarity measurement, and furtheroptimizes the search result.

Description

technical field [0001] The invention belongs to the technical field of information processing, in particular to a time series similarity search method based on segmentation weights. Background technique [0002] Data mining is to extract people's interested, implicit and previously unknown knowledge from the data of large databases. In many real-world databases, the data is often a series of observations recorded in chronological order, and the attribute values ​​of objects may change over time. Therefore, mining potentially useful knowledge from time series data has important theoretical and practical significance. The research on pattern mining of time series data mainly focuses on the similarity pattern mining of time series data. Time series data similarity pattern mining is to find time series sequences that are very similar to a given time series data pattern in the database. It has a wide range of practical values, for example: the similarity analysis of the time s...

AI Technical Summary

Problems solved by technology

The current time series data similarity pattern mining algorithm lacks effective means to further obtain satisfactory results for users, and in the above algorithms, it is difficult for users to effectively participate in the mining process of time series data similarity patterns

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