Data prediction model adjusting and optimizing method and apparatus based on LSTM network

Abstract

The invention relates to a data prediction model adjusting and optimizing method based on an LSTM network. The method include steps of preprocessing: obtaining previous N small period values of a to-be-predicted variable, and extracting data of previous multiple variables whose correlation coefficient sums are greater than a coefficient threshold in a data set to form a training set; and model training: performing N rounds of training according to a sequence of the period values from large to small to calculate an optimal solution model, wherein each round of training comprises converting thetraining set from time sequence data to a supervised learning sequence; inputting the supervised learning sequence to the LSTM network to obtain a training model of the round; and comparing a root-mean-square error obtained by the training model of the round with a root-mean-square error obtained by training of the previous round, and reserving the training model corresponding to a smaller value as the optimal solution model. The invention also relates to a data prediction model adjusting and optimizing apparatus based on the LSTM network. According to the adjusting and optimizing method and the apparatus, optimization is performed based on the LSTM network, data prediction can be realized, the calculating speed is fast, and the prediction effect is good.

Description

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AI Technical Summary

Benefits of technology

It describes a way that uses a special type of neural net called Long Short Term Memory Network (LSTM). These networks have several layers between input/output devices and memory cells. They work by processing sequences of data like speech or images from sensors connected through communication channels such as telephone lines. By removing irrelevant parts during this process, we reduce our computational resources needed compared to traditional methods. Overall, these techniques make it possible to quickly learn new patterns without being affected by any external factors affecting their accuracy.

Problems solved by technology

This patented technology solves issues related to learning sequential relationships between values over longer periods without requiring complex parameter settings or retraining procedures.

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