Multivariate time series multilayer space-time dependence modeling method based on deep learning

Abstract

The invention belongs to the field of deep learning, and discloses a multivariate time series multilayer space-time dependence modeling method based on deep learning. According to the method, a novelattention mechanism is introduced to carry out finer-grained processing on space-time dependence features extracted from different layers in the neural network; the model provided by the invention iscomposed of a stacked long-short-term neural network-convolutional neural network (LSTM-CNN). The network is composed of a spatial attention mechanism based on a CNN, a CNN-based channel attention mechanism, a time attention mechanism and an autoregressive assembly. The concept of multi-layer space-time dependence is introduced; a CNN-based channel attention mechanism and a CNN-based spatial attention mechanism are used to pay attention to space-time dependence characteristics of different layers respectively. According to the method, filtering of redundant information and effective extractionof features having greater influence on a prediction result are realized, the purpose of improving the prediction result is achieved, and the method is excellent in performance on multivariate time series data in different fields and can be extended to a task of unit time series prediction.

Description

Technical field: [0001] The invention belongs to the field of deep learning, relates to a multivariate time series prediction method based on deep learning, and belongs to the application of deep learning on time series data. Background technique: [0002] Multivariate time series commonly exist in various fields, and they have complex time and space characteristics. By using deep learning tools to represent the temporal and spatial characteristics of multivariate time series data, it can predict its future development trend, which has important practical application value in various fields such as energy consumption, traffic flow and air quality. [0003] Various methods have been proposed to capture the inherent spatiotemporal properties of multivariate time series. In the traditional time series forecasting method, the autoregressive moving average model (ARIMA) can effectively extract the long-term dependence of the sequence itself, but often ignores the spatial correla...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the current deep learning process of capturing spatio-temporal features of multivariate time series data to complete the prediction task, ignoring the differences between different layers of features and thus the impact on the prediction results

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