Method for mining rare mode of multi-dimensional time sequence data

A time-series data and rare technology, which is applied in the field of mining and analyzing rare patterns in multi-dimensional time-series data, can solve the problem that it is difficult to find the relationship between rare patterns of multi-dimensional time-series data, so as to avoid size instability, strong generalization ability, and application fields broad effect

Inactive Publication Date: 2018-05-25
BEIJING UNIV OF TECH
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Problems solved by technology

In practical applications, there may be a causal relationship between the rare patterns of different dimensions, but it is difficult to find the c

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  • Method for mining rare mode of multi-dimensional time sequence data
  • Method for mining rare mode of multi-dimensional time sequence data
  • Method for mining rare mode of multi-dimensional time sequence data

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Embodiment Construction

[0031] The present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0032] The used hardware equipment of the present invention has 1 PC machine;

[0033] like figure 1 As shown, the present invention provides a method for mining rare patterns of multi-dimensional time series data, which specifically includes the following steps:

[0034] Step 1. Obtain multi-dimensional time-series data sets in related fields, and preprocess these data.

[0035] Step 2, group the preprocessed multi-dimensional time series data according to dimensions, and use the AMRSPM algorithm to calculate the minimum rare pattern of time series data in each dimension.

[0036] Step 2.1, set the size w of the sliding window and the minimum support, and divide the w sequence according to the sliding window as the upper bound of the lattice space;

[0037] Step 2.2, split the lower bound of the lattice space, an...

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Abstract

The invention discloses a method for mining a rare mode of multi-dimensional time sequence data. Massive multi-dimensional time sequence data sets are divided into one-dimensional time sequence data sets, and the minimum rare mode is calculated for the time sequence data of each dimension by utilizing an AMRSPM algorithm; the generated minimum rare modes serve as a pattern string dictionary, and all modes are searched out for each dimension of time sequence data through a character string matching algorithm Brute-Fore or a KMP algorithm; rare sub-sequences in the multi-dimensional time sequence data are mined through clustering (AP) operation.

Description

Technical field: [0001] The invention belongs to the technical field of data mining, in particular to a method for mining and analyzing rare patterns in multi-dimensional time series data. Background technique: [0002] Multidimensional time-series data analysis has a wide range of applications in the environment, finance, medical and other fields. For example, in the field of environmental monitoring, people pay more attention to those rare or severe weather quality monitoring data than the usual weather monitoring data. Normalized air quality data appears frequently, while uncommon weather phenomena, such as severe pollution weather, are relatively rare. However, these abnormal weather events do not appear randomly, and there are some common patterns among them. Rare pattern analysis of air quality index data can reveal the law of rare weather and the relationship between different indicators, so as to provide data support for air pollution control. In the financial fiel...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/283G06F16/2465G06F16/2474G06F2216/03
Inventor 刘博刘银星
Owner BEIJING UNIV OF TECH
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