Abstract
El Nino is an important research issue in meteorology. In this paper, we propose a time series model to predict the NINO index. In this model, variational mode decomposition (VMD) is applied to extract multiple sub-signals, the long short term memory network (LSTM) is used to fit these sub-signals. Aiming at the optimization of parameters, we design a K-means neighbor particle swarm optimization (KMPSO) based on comprehensive learning particle swarm optimization (CLPSO), which optimizes the parameters of VMD and LSTM. El Nino data is widely concerned due to its strong relevance to world climate change. We conduct experiments on El Nino data, and put forward a forecast model, which has better forecast skills than other models. Experiments results demonstrate that the proposed method extends forecast time limits, and improves the accuracy prediction.
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This paper is supported by National Key R&D Program of China (2018YFB1004300).
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Shen, C., Ni, Q., Zhao, S., Zhang, M., Wang, Y. (2021). An Improved El Nino Index Forecasting Method Based on Parameters Optimization. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_43
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DOI: https://doi.org/10.1007/978-3-030-78811-7_43
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