Multi-measurement time series similarity analysis method

A time series, similarity analysis technology, applied in special data processing applications, instruments, electrical digital data processing and other directions, can solve the problem of multiple metrics and less combinations

Active Publication Date: 2014-02-12
HOHAI UNIV
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Problems solved by technology

In the research field of time series similarity analysis, there are few literatures on the combination of multiple metrics for similarity analysis.

Method used

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  • Multi-measurement time series similarity analysis method
  • Multi-measurement time series similarity analysis method
  • Multi-measurement time series similarity analysis method

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Embodiment Construction

[0016] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0017] The present invention is aimed at the k-nearest neighbor search problem, that is, querying the top k sequences (subsequences) most similar to a specified sequence. From the perspective of classification, k-nearest neighbor similarity search can be regarded as using similarity metrics to divide time series into the first similar sequence (subsequence), the second similar sequence (subsequence), ..., the kth similar sequence (subsequence) sequence) and dissimilar sequences (subsequences). Us...

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Abstract

The invention discloses a multi-measurement time series similarity analysis method applicable to k-neighbor inquires of a time series. A multi-single-similarity-measurement method is chosen according to the analysis requirement, each single similarity measurement is used to analyze and inquire an m-neighbor sequence or subsequence of the sequence, pruning the m-neighbor sequence or subsequence under each similarity measurement to obtain a candidate similarity sequence or subsequence, and combining the candidate similarity sequence or subsequence by using a multiple-classifier combination method with advantage weight to obtain the k-neighbor sequence of the inquired sequence. Compared with the single similarity measurement, the similarity analysis of combined multiple measurements can obtain a more comprehensive analysis result. The multiple-classifier combination method with advantage weight regulates the ranking score according to the difference of the similarity distance between the adjacent candidate similarity sequence or subsequence and the inquired sequence while using a BORDA counting method for reference, so as to reflect the specific difference of similarity of the candidate similarity sequence or subsequence.

Description

technical field [0001] The invention relates to a multi-metric time series similarity analysis method, in particular to a method capable of performing multi-metric combination k-nearest neighbor similarity time series analysis, and belongs to the technical field of data mining. Background technique [0002] Time series similarity search is to find and find time series similar to a given pattern in the time series database. The process of finding similar subsequences is often encountered in practical problems. For example, in the human genome project, from DNA gene sequence Find sub-fragments similar to a given gene segment in the given gene segment, and conduct research based on genetic similarity; find out similar product sales patterns based on the sales records of various commodities, and formulate similar sales strategies based on the sales patterns of similar products strategies, etc.; find out the same precursors of natural disasters, so as to conduct decision-making r...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/90348
Inventor 王继民朱跃龙李士进万定生冯钧
Owner HOHAI UNIV
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