Time sequence missing value filling method based on bidirectional cyclic codec neural network

A time series and codec technology, which is applied in the field of time series missing value filling based on bidirectional cyclic codec neural network, which can solve problems such as the impact of changes in the filling effect of spatiotemporal relationships

Active Publication Date: 2021-02-26
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

It can overcome the shortcomings of existing time series filling methods that it is difficult to correctly model the spatio-temporal relationship in time series with missing values, and the filling effect is greatly affected by the change of missing rate

Method used

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  • Time sequence missing value filling method based on bidirectional cyclic codec neural network
  • Time sequence missing value filling method based on bidirectional cyclic codec neural network
  • Time sequence missing value filling method based on bidirectional cyclic codec neural network

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Embodiment Construction

[0054] All artificial intelligence-related terms used herein have the same meanings as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0055] The invention provides a time series missing value filling method based on a two-way loop codec neural network to fill the sensor time series. Examples provided by this application, such as figure 1 Shown is a schematic diagram of the scenario for missing value filling of sensor multidimensional time series. The acquisition device is connected with several monitoring devices, and the data is collected at shorter intervals and uploaded to the server at longer intervals. The data received by the server can be regarded as a time series of equal length. If there is no missing time series, the server will directly store the series into the historical database; if there are missing data, the server will input the series into the filling module, and the filling module will store the fi...

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Abstract

The invention provides a time sequence missing value filling method based on a bidirectional cyclic codec neural network. According to the method, an auto-encoder and a recurrent neural network are combined, and modeling of a time sequence containing missing values can be achieved; according to the method, the difference between a filling sequence and a label sequence is measured through two training losses, and an encoder and a decoder are reversely updated in an asynchronous mode; according to the method, the response of the gating unit amplification network to missing data is coordinated. According to the method, the problems that the space-time relationship of the time sequence containing the missing value cannot be correctly modeled by a common method and the filling effect is sensitive to the change of the missing rate are solved.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a method for filling missing values ​​of time series based on a bidirectional cyclic codec neural network. Background technique [0002] In the application tasks of multi-dimensional time series in the industrial Internet of Things, such as context recognition, predictive maintenance, anomaly detection, etc., a complete time series is the prerequisite for the smooth progress of the task. However, a large number of equipment accesses and unstable environments lead to the prevalence of missing values ​​in the multidimensional time series of the Industrial Internet of Things. The existing multidimensional time series filling methods include mean filling, clustering filling, regression filling and so on. The mean filling effect depends on the difference between data points, and the filling accuracy is not high, especially when continuous missing occurs, it is easy to cause lar...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F16/9537G06K9/62G06N3/08
CPCG06F16/2474G06N3/08G06F16/9537G06F18/214
Inventor 邬惠峰丘嘉晨孙丹枫
Owner HANGZHOU DIANZI UNIV
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