Abstract
This paper proposes a multi-step ahead time series forecasting based on the improved process neural network. The intelligent algorithm particle swarm optimization (PSO) is used to overcome the potential disadvantages of the neural network, such as slow convergence speed and derivative local minima. Firstly, the theoretical analysis of the PSO is given to optimize the multilayer perceptron (MLP) neural network architecture. Secondly, the theoretical analysis and processing flow about the MLP architecture optimization is given. Thirdly, the performance criteria are applied to verify the performance of the proposed approach. Finally, the experimental evaluation based on a typically chaotic time series with rich spectrum information is utilized to demonstrate that the proposed approach has comparative results and superior on forecasting accuracy comparing to the traditional methods.
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Acknowledgements
This project is supported by the National Natural Science Foundation of China (NSFC) (No. 61806087, 61902158), Natural science youth fund of Jiangsu Province (No. BK20150471), Jiangsu Province Natural Science Research Projects (No.17KJB470002) and Jiangsu University of Science and Technology Youth Science and Technology Polytechnic Innovation Project (No. 1132931804).
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Shao, H., Hu, C., Deng, X., Jiang, D. (2021). Multi-step Ahead Time Series Forecasting Based on the Improved Process Neural Networks. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_38
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DOI: https://doi.org/10.1007/978-981-15-3753-0_38
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