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A novel metal futures forecasting system based on wavelet packet decomposition and stochastic deep learning model

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Abstract

The forecasting technique of metals futures prices is helpful to realize the early warning for exporter and importer countries, market traders and government regulation. In order to optimize the accuracy of metal futures prices prediction, this paper establishes a novel hybrid model, which combines signal decomposition theory, stochastic theory and deep learning methods. The primary modelling process of the proposed model involves three main steps. In step I, the proposed decomposition method is employed to preprocess the raw metal futures data. The time strength weight algorithm based on the long-term memory model is trained for each subseries in step II. In step III, the final forecasting results are obtained by inverse transform after the stochastic deep learning model for each sub-series is constructed by preprocessing and training. The proposed model exhibits superior performance in metal futures forecasting by analyzing the results with multiplied practices and an original error evaluation approach called multi-scale Jensen-Shannon divergence. It can get higher forecasting accuracy than the corresponding single models and the existing hybrid models.

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Acknowledgements

The authors would like to thank the financial supports from the funds of North China University of Technology, PR China (Grant No.110051360002), National Natural Science Foundation of China (Grant No.61903006) and the Fundamental Research Funds for the Universities in Beijing (Grant No.110052972027/007).

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  1. Department of Statistics, College of Science, North China University of Technology, 100144, Beijing, People’s Republic of China

    Jie Wang

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Wang, J. A novel metal futures forecasting system based on wavelet packet decomposition and stochastic deep learning model. Appl Intell 52, 9334–9352 (2022). https://doi.org/10.1007/s10489-021-03083-x

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