E3S Web Conf.
Volume 256, 20212021 International Conference on Power System and Energy Internet (PoSEI2021)
|Number of page(s)||4|
|Section||Energy Internet R&D and Smart Energy Application|
|Published online||10 May 2021|
Multivariate time series prediction of high dimensional data based on deep reinforcement learning
1 Big Data Center of State Grid Corporation of China, Beijing 100052, China
2 Beijing Sgitg Accenture Information Technology Center Co., Ltd, Beijing, 100052 China
* E-mail: email@example.com
In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.
© The Authors, published by EDP Sciences, 2021
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