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Long-term prediction of time series based on fuzzy time series and information granulation

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Abstract

Time-series prediction involves forecasting future data by analyzing and modeling historical data. The prediction process involves analyzing and mining various features hidden in the data to predict future data. Compared with one-step forecasting, long-term forecasting is urgently needed, which contributes to capturing the overall picture of future trends and enables discovering prospective ranges and development patterns. This study presents a new long-term forecasting model named the TIG_FTS_SEL model, which is developed by integrating trend-based information granules (TIGs), fuzzy time series, and ensemble learning. First, a time series is converted into a series of equal-length trend-based information granules to capture the fluctuation range and trend information effectively. Then the trend-based information granules are fuzzified to form fuzzy time series, which contributes to realizing the long-term prediction at a high abstract level. Furthermore, different models are used to establish an ensemble long-term forecasting approach by introducing a selection strategy for individual models. The ensemble method performs the prediction tasks using part models with solid prediction performances while disregarding the remaining models. Finally, the developed model is verified by experiments on different time-series datasets. The results demonstrate the sound prediction performance and efficiency of the proposed model.

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Data availability

The datasets analyzed in this study are publicly available datasets utilized in previous literature, including Mackey-Glass time series (https://ww2.mathworks.cn/help/fuzzy/predict-chaotic-time-series-code.html;jsessionid=5f211830b69e40ff46d8380efa3e), MT time series (https://github.com/FinYang/tsdl), Zuerich monthly sunspot numbers (https://github.com/FinYang/tsdl), Daily temperature time series (https://geographic.org/globalweather/britishcolumbia/cowichanlakeforestry040.html), Stock index time series (https://finance.yahoo.com/quote/), Monthly mean total sunspot time series (http://www.sidc.be/silso/datafiles), and historical levels of Lake Erie time series (http://www.glisaclimate.org/projects/484/page/2536).

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Acknowledgements

This work was supported by the Natural Science Foundation of China under Grant 62173053.

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Authors and Affiliations

  1. School of Science, Dalian Maritime University, Dalian, 116026, China

    Yunzhen Liu & Lidong Wang

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  1. Yunzhen Liu
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  2. Lidong Wang
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Contributions

YL: conceptualization, methodology, investigation, formal analysis, data collection, software, validation, writing—original draft. LW: supervision, methodology, writing—review and editing.

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Correspondence to Lidong Wang.

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Liu, Y., Wang, L. Long-term prediction of time series based on fuzzy time series and information granulation. Granul. Comput. 9, 46 (2024). https://doi.org/10.1007/s41066-024-00476-4

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