Embedded index based hydrological time series similarity searching method

Abstract

The present invention discloses an embedded index based hydrological time series similarity searching method. The method is carried out by the following steps: calculating corresponding embedded index vectors of each position in an original time series in an off-line preparation stage, wherein in the off-line preparation stage, the hydrological time series flood peak segmentation, serial clustering, initial reference sequence set generation, reference set training and time series embedded index calculation are realized; and calculating the index vectors by using a query sequence and a reference set sequence in an on-line searching stage, performing searching in an embedded index Euclidean vector space of the original series to find a relatively similar point as a candidate point set, refining candidate points, and then performing original DTW (dynamic time warping) measurement to find a final similar sequence. According to the embedded index based hydrological time series similarity searching method, the similarity searching is mapped to the Euclidean vector space to perform searching, thereby greatly improving the search efficiency.

Description

technical field [0001] The invention relates to a hydrological time series similarity search method based on an embedded index, belonging to the fields of data mining and information technology. Background technique [0002] With the continuous growth of hydrological time series data, how to quickly and accurately find out the hydrological process similar to a given time period from the historical hydrological database is a topic worthy of further study. In particular, in flood control, it is often necessary to quickly find similar flood peak processes in historical flood sequences. At this time, the similarity analysis of hydrological time series has more important practical significance. The similarity measurement of time series is the basic problem of time series data mining. The main methods include Euclidean distance, dynamic pattern matching, dynamic time warping (DTW) distance, slope distance and so on. Among them, dynamic pattern matching can find out the general po...

AI Technical Summary

Problems solved by technology

Among them, dynamic pattern matching can find out the general position of similar sequences, but cannot accurately measure

Euclidean distance can be measured accurately but is susceptible to noise and curvature on the time axis, while DTW distance has the disadvantage of high time complexity

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