Time sequence classification method based on deep reinforcement learning

Abstract

The invention discloses a time sequence classification method based on deep reinforcement learning, and the method comprises the following steps: collecting a plurality of time sequences, obtaining sample data, and carrying out the preprocessing of the sample data; constructing a deep residual network, and updating the deep residual network through a deep reinforcement learning method according tothe preprocessed sample data; and inputting a to-be-tested time sequence into the updated deep residual network to obtain a classification result of the time sequence. According to the method, the samples are input into the deep reinforcement learning network in a disordered sequence, so that the deep reinforcement learning network is more robust, the optimal strategy of time sequence classification is searched in a reward and punishment setting mode, and the classification accuracy is high.

Description

technical field [0001] The invention belongs to the field of time series classification, and in particular relates to a time series classification method based on deep reinforcement learning. Background technique [0002] With the improvement of sensing technology and monitoring technology, our daily life is constantly generating time-series data, such as stock prices, weather readings, biological observations, health monitoring data, etc. In the era of big data, there is an increasing need to extract knowledge from time series data. One of the main tasks is time series classification, which is to predict its corresponding category labels through existing time series data. Most existing time series classification methods can be divided into distance-based methods and feature-based methods. However, the computational complexity of existing time series classification methods is high, and the classification decision accuracy of time series is not high. Contents of the invent...

AI Technical Summary

Problems solved by technology

However, the existing time series classification methods have high computational complexity, and the classification decision accuracy of time series is not high.

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