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A deep granular network with adaptive unequal-length granulation strategy for long-term time series forecasting and its industrial applications

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Abstract

An accurate long-term forecasting for some time series in industrial production is substantially significant for improving the economic efficiency of industry enterprise. In this study, a granular computing (GrC)-based deep learning framework is proposed for long-term time series forecasting, which consists of two stages i.e., the adaptive data granulation and the GrC-based deep model construction. In the first stage, for automatically generating the basic information granules with unequal time span adaptively from data, a stacked sparse auto-encoders granulation network is designed, in which the starting and ending points of a granule are adaptively determined by setting a single neuron in the last hidden layer after multi-layer feature extraction. Then, the second stage sees a definition of a partially overlapping sub-block basis (POSB) matrix to extract the features of these granules, based on which a deep sparse coding feature decomposition-based long-term forecasting model is constructed to transform the unequal-length granules into a product of a POSB matrix and a coefficient matrix layer by layer to serve for forecasting. To verify the effectiveness of the proposed method, two synthetic datasets, two real-world datasets and two practical industrial datasets are employed. The experimental results demonstrate that the proposed method outperforms other data-driven ones on long-term time series forecasting, particularly in an industrial case.

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Acknowledgements

This work was supported by the National Key R&D Program of China under Grant 2017YFA0700300, the National Natural Sciences Foundation of China under Grant 61833003, Grant 61773085, Grant 61533005, Grant U1908218, the Fundamental Research Funds for the Central Universities under Grant DUT18TD07, and the Outstanding Youth Sci-Tech Talent Program of Dalian under Grant 2018RJ01.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Ministry of Education, Dalian University of Technology, Dalian, 116024, China

    Qiang Wang, Long Chen, Jun Zhao & Wei Wang

  2. School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, China

    Qiang Wang, Long Chen, Jun Zhao & Wei Wang

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  1. Qiang Wang
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Correspondence to Jun Zhao.

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Appendix

Appendix

See Tables 11 and 12.

Table 11 Nomenclatures used in this study
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Table 12 Acronyms used in this study
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Wang, Q., Chen, L., Zhao, J. et al. A deep granular network with adaptive unequal-length granulation strategy for long-term time series forecasting and its industrial applications. Artif Intell Rev 53, 5353–5381 (2020). https://doi.org/10.1007/s10462-020-09822-9

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