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Time-series similarity measurement method under data missing

A similarity measurement and time series technology, which is 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: 2014-06-25
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 data missing
  • Time-series similarity measurement method under data missing
  • Time-series similarity measurement method under data missing

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Embodiment Construction

[0011] The present invention will be described in further 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 the two time series in pairs. Suppose the mth and nth data are extracted for the two time series respectively to obtain x jm ,x jn And x im ,x in Of Correct. And the constraint of each data is

[0014] 2) For this For each pair in the data (x im ,x in } And {x jm ,x jn }, divided into the following 5 cases to consider calculating the similarity interval, which is called the first-order similarity:

[0015] (1) If the data is not missing, follow the formula below:

[0016] s mn ′ ( { x im , x in } , { x jm , x jn } ) = x im x jm + x in x jn ...

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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 method for calculating the similarity of time series in computer information processing, in particular to the calculation of two time series when there are one or more missing data and the physical constraint of the data is [0, upper limit] The 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. Time series similarity can find many similar time series in the same field, thus providing an excellent basis for the analysis of physical and social phenomena. Favorable data. The current time series similarity methods are mainly aimed at the situation where there is no missing data. If the data is missing, use average replacement, trend extrapolation, exponential smoothing, etc. to make up, but these make up require prior knowledge, which makes it difficult to guarantee the data The ...

Claims

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Application Information

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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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