Time series similarity measurement method under missing data

A similarity measurement, time series technology, applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve the problem that time series measurement cannot be applied, and achieve the effect of simple method

Active Publication Date: 2015-08-26
ZHEJIANG UNIV
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Problems solved by technology

[0003] In order to overcome the fact that existing time series metrics cannot be applied to the case of missing data, the present invention proposes a method that can calculate time series similarity in any case of missing data

Method used

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  • Time series similarity measurement method under missing data
  • Time series similarity measurement method under missing data
  • Time series similarity measurement method under missing data

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Embodiment Construction

[0011] The present invention will be further described in detail below.

[0012] Suppose for two time series X i =(x i1 ,x i2 ...) and X j =(x j1 ,x j2 ...), the length of the time series is N, each value of the time series has an upper limit x, and the lower limit is 0, the similarity calculation method is as follows:

[0013] 1) Extract the data pairs of two time series in pairs, and extract the mth and nth data of the two time series respectively, and get x jm ,x jn and x im ,x in , in total right. and each data constraint is

[0014] 2) For this For each pair {x in the data im ,x in} and {x jm ,x jn}, divided into the following five situations to consider the calculation of the similarity interval, which is called the first-order similarity:

[0015] (1) If none of the data is missing, follow the formula below:

[0016] s mn ′ ( { x im ...

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Abstract

The invention discloses a time-series similarity measurement method capable of adapting to missing data. According to the method, data pairs are extracted from two original time series in pairs and are divided into five types according to data missing conditions, and first-order similarity intervals are calculated respectively; intervals are extracted from the first-order similarity intervals in pairs, second-order similarity is figured out, and second-order similarity vector quantities are obtained; at last, the second-order similarity vector quantities are averaged to obtain final similarity of the two time series. The method can adapt to multiple scenes, is simple and does not have any requirement for data integrity.

Description

technical field [0001] The present invention relates to a time series similarity calculation method in computer information processing, in particular to calculating the time series similarity between two time series when there are one or more missing data and the physical constraints of the data are [0, upper limit]. method of similarity. Background technique [0002] A large number of time series exist in human society and nature, such as financial time series, traffic time series, temperature time series, etc. The similarity of time series can find many similar time series in the same field, thus providing an extremely useful tool for the analysis of physical and social phenomena. Favorable data. The current time series similarity method is mainly aimed at the situation where there is no missing data. If the data is missing, the average value replacement, trend extrapolation method, exponential smoothing method, etc. are used to make up for it. However, these compensation...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 祁宏生王殿海许骏叶盈韦薇郑正非蔡正义
Owner ZHEJIANG UNIV
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