CNN-based time sequence prediction method and model determination method

Abstract

The invention relates to a CNN-based time sequence prediction method and a model determination method. The CNN-based time sequence prediction method comprises the steps: acquiring historical time sequence data, determining periodic parameters according to periodic characteristics of the historical time sequence data, wherein the periodic parameters comprise periodic types and periodic durations corresponding to the periodic types; determining component data corresponding to the prediction time point in the historical time sequence data based on the prediction time point, the historical time sequence data, the period parameters and a preset cycle span, the component data comprising the data of the closest time period and the period data; and predicting the component data by adopting the determined CNN model to obtain a prediction result corresponding to the prediction time point. By adopting the CNN-based time sequence method, the subsequent time sequence information can be efficientlyand accurately predicted.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a CNN-based time series forecasting method, device, computer equipment and storage medium, and a CNN-based time series forecasting model determination method, device, computer equipment and storage medium. Background technique [0002] Time series refers to the sequence of the values ​​of the same statistical index arranged in the order of their occurrence time. The main purpose of time series forecasting is to predict the future based on existing historical data. With the development of artificial neural network, the application of recurrent neural network (RNN) in time series forecasting has become more and more mature. However, at present, for a time series, RNN can only extract long-term and short-term relevant information of the data from the time trajectory, and cannot give feedback on the periodic information of the sequence. There are problems such as time-con...

AI Technical Summary

Problems solved by technology

However, at present, for a time series, RNN can only extract long-term and short-term relevant information of the data from the time trajectory, and cannot give feedback on the periodic information of the sequence. There are problems such as time-consuming and inaccurate prediction results.

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