Time sequence index based on trends

Abstract

The invention provides an index mode based on change trends of time sequences (determined time sequences and undetermined time sequences) and first-order connectivity indexes, and the index mode has great significance to time sequence prediction, classification, data mining, knowledge discovery and the like. The index mode solves the problems of high data redundancy or low match accuracy and low index efficiency caused by the time sequence space indexes, precise query, similarity query, clustering and classification of the time sequences can be finished effectively through the index, and the time complexity and space complexity of sequence query, clustering and classification are lowered greatly. According to the index mode, firstly interval segmentation is conducted on the time sequences and time dimensions, short trend symbol sequences are generated in a mapping mode according to the change trends of the time sequences in all sections, then the first-order connectivity indexes of the section rising trend, section descending trend, section wave-crest trend, section wave-trough trend and section gentle trend are calculated for the symbol sequences, and finally a B-Tree index of a time sequence database is built by the adoption of the one-order indexes of the five trends.

Description

Technical field [0001] The invention relates to a time series indexing method, which can effectively establish an index for the time series in a database, quickly retrieve the corresponding time series through the index, and support the similarity matching query of the sequence through the index. Background technique [0002] Due to the huge amount of data in the time series database, in order to quickly complete the retrieval and similarity matching, it is necessary to index the time series. Due to the high-dimensional nature of time series, and the Euclidean spatial distance is mostly used for the time series similarity measurement, most of the index methods also use the spatial index structure. From a large perspective, the spatial data index technology used in time series can be divided into two types: tree structure (including R tree, K-D tree, quad tree) and grid file, mainly including F-index, ST index, vp-tree , FastMap, etc., but these indexing methods cause high d...

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Problems solved by technology

[0003] In order to solve the problems of high data redundancy and low indexing efficiency in the existing time series indexing method, this invention proposes an indexing method based on time series trend and first-order connectivity index, which can be based on the changing trend of time series , within the polynomial time complexity, the time series is reduced to the first-order connectivity index value of the five trends, and then the B-Tree index is established for the time series database through the first-order connectivity index of the five trends, so as to achieve fast Index the time series, and effectively support precise query and similarity matching query through the index

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