Abstract
Fuzzy time series models have good performance for uncertain time series with fuzziness or approximation. However, the fuzzy time series model needs to be further optimized when dealing with time series with trends and disturbances(mainly noise). To solve the problems, this paper proposes a novel fuzzy time series model(NFTSM) based on an improved sparrow search algorithm(ISSA) and complete ensemble empirical mode decomposition with adaptive noises(CEEMDAN). First, NFTSM detects the trends of the time series. Second, the improved Chen’s predictor(ICP) forecasts the time series with no trend, and ISSA parts the universe of discourse accurately in ICP. For time series with a trend, CEEMDAN decomposes it into multiple time series. Then, detect disturbance time series in multiple time series and discard them. The remaining multiple time series are divided into trend and non-trend time series. ICP predicts non-trend time series, and the least-square method fits and predicts trend time series. At last, the prediction results are directly obtained by ICP or reconstructed from multiple time series prediction results. The simulation experiments using time series of Alabama University enrollments and NASDAQ closing prices show that NFTSM has good performance.
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Acknowledgments
This work was supported by the Graduate Teaching Reform Research Program of Chongqing Municipal Education Commission (No.YJG212022), Chongqing Research and Innovation Project of Graduate Students(No.CYS21326) and National Natural Science Foundation of China (No. 61876201).
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Xian, S., Lei, H., Chen, K. et al. A novel fuzzy time series model based on improved sparrow search algorithm and CEEMDAN. Appl Intell 53, 11300–11327 (2023). https://doi.org/10.1007/s10489-022-04036-8
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DOI: https://doi.org/10.1007/s10489-022-04036-8