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Double-Input Rule Modules Stacked Deep Interval Type-2 Fuzzy Model with Application to Time Series Forecasting

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Abstract

The data-driven forecasting methods/models usually face two important tasks: one is to effectively deal with the uncertainties and/or noise in the data for improving the accuracy, another is to increase the models’ interpretability. In order to realize these two objectives, this study presents one novel double-input rule modules (DIRMs) stacked deep interval type-2 fuzzy model (DIT2FM) for forecasting applications. Firstly, the stacked structure of the proposed deep fuzzy model, abbreviated as IT2DIRM-DFM, is presented. This deep fuzzy model is constructed by stacking the interval type-2 fuzzy sets-based double-input rule modules (IT2DIRMs). It can not only efficiently handle high levels of uncertainties exited in the observed data by the type-2 fuzzy scheme, but also has better interpretable ability than the conventional deep models through clearly observing which rules are fired and how the fired degrees are. Then, the data-driven learning strategy for the IT2DIRM-DFM is proposed. In the proposed learning strategy, the data generation processes for all the stacked layers and the constrained least square method based design of the DIRMs are presented in detail. Finally, the proposed model is successfully applied to two real-world forecasting applications, and comparison results with the other forecasting models are also given. Both the statistical analysis and the experimental comparisons validate the superiorities of the proposed model over the other comparative models.

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Acknowledgements

This study is partly supported by the National Natural Science Foundation of China (Nos. 61903226, 62076150), the Taishan Scholar Project of Shandong Province (No. TSQN201812092), the Key Research and Development Program of Shandong Province (Nos. 2019GGX101072, 2019JZZY010115), and the Youth Innovation Technology Project of Higher School in Shandong Province (No. 2019KJN005).

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Authors and Affiliations

  1. Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, 250101, China

    Wei Peng, Changgeng Zhou, Chengdong Li, Xiaoping Deng & Guiqing Zhang

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  1. Wei Peng
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  2. Changgeng Zhou
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  3. Chengdong Li
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  4. Xiaoping Deng
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  5. Guiqing Zhang
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Correspondence to Chengdong Li.

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Peng, W., Zhou, C., Li, C. et al. Double-Input Rule Modules Stacked Deep Interval Type-2 Fuzzy Model with Application to Time Series Forecasting. Int. J. Fuzzy Syst. 23, 1326–1346 (2021). https://doi.org/10.1007/s40815-021-01087-w

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