CNN-based time sequence prediction method and model determination method

A technology of time series and models, applied in the computer field, can solve problems such as inaccurate prediction results, inability to give feedback on sequence periodic information, and time-consuming problems, so as to save time for model training and testing, speed up, and improve efficiency. effect of ability

Active Publication Date: 2019-09-13
HUNAN UNIV
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  • Claims
  • Application Information

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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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  • CNN-based time sequence prediction method and model determination method
  • CNN-based time sequence prediction method and model determination method
  • CNN-based time sequence prediction method and model determination method

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Embodiment Construction

[0040] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0041] In one embodiment, such as figure 1 As shown, a CNN-based time series prediction method is provided, including the following steps S102 to S106.

[0042] S102. Acquire historical time series data, and determine period parameters according to period characteristics of the historical time series data, where the period parameters include period types and corresponding period durations.

[0043] S104. Based on the predicted time point, the historical time series data, the cycle parameter and the preset cycle span, determine the component data corresponding to the predi...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04
CPCG06N3/045
Inventor 陈岑王康李克勤段明星刘楚波阳王东李肯立
Owner HUNAN UNIV
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