Multivariate time series classification method and system based on full convolution attention

A technology of multivariate time series and classification method, applied in the field of multivariate time series classification method and system based on full convolution attention, can solve the problems of few multivariate time series classification technology and time series classification technology unable to solve multivariate dependencies and so on. , to reduce the effect of

Pending Publication Date: 2021-04-27
ENJOYOR CO LTD
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Problems solved by technology

The above is aimed at the research of time series classification technology, and there are few studies on multivariate time serie...

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  • Multivariate time series classification method and system based on full convolution attention
  • Multivariate time series classification method and system based on full convolution attention
  • Multivariate time series classification method and system based on full convolution attention

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Embodiment

[0077] Example: such as figure 1 As shown, a multivariate time series classification system based on fully convolutional attention includes a multivariate time series preprocessing unit, a multivariate time series feature extraction unit and a multivariate time series classification unit.

[0078] The multivariate time series preprocessing unit is used to preprocess multivariate time series data into multivariate time series vectors.

[0079] The multivariate time series feature extraction unit is used to extract and fuse the multi-view feature of the multivariate time series vector by using the full convolutional neural network and the attention model, and obtain the time variable vector of the fusion multi-view; specifically includes: a full convolutional neural network module, Variable attention module, time attention module, weight matrix module;

[0080] The fully convolutional neural network module is used to extract local and non-local variable features, local and non-...

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Abstract

The invention relates to a multivariate time sequence classification method and system based on full convolution attention, and the method comprises the steps: capturing the local variable features of a multivariate time sequence through employing a 2D convolution filter through employing a design idea of full convolution in the field of images, so as to learn the linkage relation between adjacent variables; capturing local time features of the multivariate time sequence by using a 2D convolution filter to learn trend information between adjacent time, and weakening the influence of mutation information on a result; adopting a convolution and self-attention model, obtaining multiple local features through multi-kernel convolution, enabling the self-attention model to calculate the weights of the multiple local features and non-local features, and providing different visual angles for reviewing multivariate time series data; adopting an attention model to respectively fuse variables and time characteristics of corresponding visual angles, and learning a global dependency relationship of the variables and a global dependency relationship of time; adopting a weight matrix method to fuse multi-view features, and learning more comprehensive and more accurate time variable interaction features.

Description

technical field [0001] The invention relates to a packaging box, in particular to a method and system for multivariate time series classification based on full convolutional attention. Background technique [0002] A time series is a set of random variables sorted by time, which is usually the result of observing a certain underlying process at a given sampling rate in an equally spaced time period. Time series data essentially reflects the trend of one or some random variables changing over time, and the core of the time series classification method is to mine this rule from the data and use it to make category predictions for future data . In real life, observing data at a series of time points is a common activity, and research fields such as agriculture, commerce, meteorology, military and medical care contain a large amount of time series data. In summary, time series data is currently being generated at an unpredictable rate in almost every application domain in real...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/08
CPCG06N3/08G06F18/24G06F18/253G06F18/214
Inventor 金佳佳韩潇丁锴王开红李建元陈涛
Owner ENJOYOR CO LTD
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