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A novel fuzzy time series model based on improved sparrow search algorithm and CEEMDAN

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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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References

  1. Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst 54(1):1–9

    Google Scholar 

  2. Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series–part II. Fuzzy Sets Syst 62(1):1–8

    Google Scholar 

  3. Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst 54(3):269–277

    MathSciNet  MATH  Google Scholar 

  4. Zadeh LA (1965) Fuzzy set. Inf Control 8(3):338–353

    MATH  Google Scholar 

  5. Chen SM (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81(3):311–319

    Google Scholar 

  6. Lee HS, Chou MT (2004) Fuzzy forecasting based on fuzzy time series. Int J Comput Math 81(7):781–789

    MathSciNet  MATH  Google Scholar 

  7. Panigrahi S, Behera HS (2020) A study on leading machine learning techniques for high order fuzzy time series forecasting. Fuzzy Sets Syst 87:103245. https://doi.org/10.1016/j.engappai.2019.103245https://doi.org/10.1016/j.engappai.2019.103245

    Google Scholar 

  8. Kumar N, Susan S (2021) Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19. Appl Soft Comput 110:107611. https://doi.org/10.1016/j.asoc.2021.1076115https://doi.org/10.1016/j.asoc.2021.1076115

    Google Scholar 

  9. Chen S-M, Zou XY, Gunawan GC (2019) Fuzzy time series forecasting based on proportions of intervals and particle swarm optimization techniques. Inf Sci 500:127–139

    MathSciNet  Google Scholar 

  10. Xian SD, Li TJ, Cheng Y (2020) A novel fuzzy time series forecasting model based on the hybrid wolf pack algorithm and ordered weighted averaging aggregation operator. Int J Fuzzy Syst 22(6):1832–1850

    Google Scholar 

  11. Xian SD, Zhang JF, Xiao Y, Pang J (2018) A novel fuzzy time series forecasting method based on the improved artificial fish swarm optimization algorithm. Soft Comput 22(12):3907– 3917

    Google Scholar 

  12. Chen SM, Zou XY, Gunawan GC (2019) Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm. Inf Sci 484:350–366

    Google Scholar 

  13. Cheng ZW, Song HH, Wang JQ et al (2021) Hybrid firefly algorithm with grouping attraction for constrained optimization problem. Knowl Based Syst 220:106937. https://doi.org/10.1016/j.knosys.2021.106937

    Google Scholar 

  14. Gao R, Duru O (2020) Parsimonious fuzzy time series modelling. Expert Syst Appl 56:113447. https://doi.org/10.1016/j.eswa.2020.113447https://doi.org/10.1016/j.eswa.2020.113447

    Google Scholar 

  15. Chen MR, Zeng GQ, Lu KD (2019) A many-objective population extremal optimization algorithm with an adaptive hybrid mutation operation. Inf Sci 498:62–90

    MathSciNet  MATH  Google Scholar 

  16. Lu KD, Zhou WN, Zeng GQ, Zheng YY (2019) Constrained population extremal optimization-based robust load frequency control of multi-area interconnected power system. Int J Electr Power Energy Syst 105:249–271

    Google Scholar 

  17. Zhou JF, Zhang YH, Zhang YB et al (2022) Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning. Appl Energy 314:118877. https://doi.org/10.1016/j.apenergy.2022.118877

    Google Scholar 

  18. Jiang P, Yang HF, Li RR, Li C (2020) Inbound tourism demand forecasting framework based on fuzzy time series and advanced optimization algorithm. Appl Soft Comput 92:106320. https://doi.org/10.1016/j.asoc.2020.106320

    Google Scholar 

  19. Zhang AS, Shi WZ (2020) Mining significant fuzzy association rules with differential evolution algorithm. Appl Soft Comput 97:105518. https://doi.org/10.1016/j.asoc.2019.105518

    Google Scholar 

  20. Caprio DD, Ebrahimnejad A, Alrezaamiri H et al (2020) A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alex Eng J 61(5):3403–3415

    Google Scholar 

  21. Xie CY, Nguyen H, Bui XN, Nguyen VT, Zhou J (2021) Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms. J Rock Mech Geotech Eng 13(6):1452–1465

    Google Scholar 

  22. Zhang NN, Luo C (2019) Adaptive online time series prediction based on a novel dynamic fuzzy cognitive map. J Intell Fuzzy Syst 36(6):5291–5303

    Google Scholar 

  23. Singh G, Singh A (2021) Extension of particle swarm optimization algorithm for solving transportation problem in fuzzy environment. Appl Soft Comput 10:107619. https://doi.org/10.1016/j.asoc.2021.107619

    Google Scholar 

  24. Mahrooghi A, Lakzian E (2021) Optimization of Wells turbine performance using a hybrid artificial neural fuzzy inference system (ANFIS) - Genetic algorithm (GA). Ocean Eng 226:108861. https://doi.org/10.1016/j.oceaneng.2021.108861

    Google Scholar 

  25. Egrioglu E, Bas E, Yolcu U, Chen MY (2020) Picture fuzzy time series: Defining, modeling and creating a new forecasting method. Eng Appl Artif Intell 88:103367. https://doi.org/10.1016/j.engappai.2019.103367

    Google Scholar 

  26. Yolcu OC, Alpaslan F (2018) Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process. Appl Soft Comput 66:18–33

    Google Scholar 

  27. Du S, Wu M, Chen LF, Pedrycz W (2021) Prediction model of burn-through point with fuzzy time series for iron ore sintering process. Eng Appl Artif Intell 102:104259. https://doi.org/10.1016/j.engappai.2021.104259

    Google Scholar 

  28. Cheng CH, Yang JH (2018) Fuzzy time-series model based on rough set rule induction for forecasting stock price. Neurocomputing 302:33–45

    Google Scholar 

  29. Silva PCDLE, Severiano CA, Alves MA et al (2020) Forecasting in non-stationary environments with fuzzy time series. Appl Soft Comput 97:106825. https://doi.org/10.1016/j.asoc.2020.106825

    Google Scholar 

  30. Severiano CA et al (2021) Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems. Appl Mech Mater 171:764–783

    Google Scholar 

  31. Fan XS, Wang Y, Zhang MY (2020) Network traffic forecasting model based on long-term intuitionistic fuzzy time series. Inf Sci 506:131–147

    Google Scholar 

  32. Xue JK, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control 8(1):22–34. https://doi.org/10.1080/21642583.2019.1708830

    Google Scholar 

  33. Zeng ZQ, Zhang M, Hong ZY, Zhang HH, Zhu H (2022) Enhancing differential evolution with a target vector replacement strategy. Comput Stand Interfaces 82:103631. https://doi.org/10.1016/j.csi.2022.103631https://doi.org/10.1016/j.csi.2022.103631

    Google Scholar 

  34. Saeed MH, Fangzong W, Salem S et al (2021) Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources. Energy Rep 7:8912–8928

    Google Scholar 

  35. Hao P, Sobhani B (2021) Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model. Int J Hydrog Energy 47 (63):36454–36465

    Google Scholar 

  36. Fernandes PB, Oliveira RCL et al (2022) Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity. Appl Soft Comput 116:108108. https://doi.org/10.1016/j.asoc.2021.108108

    Google Scholar 

  37. Machado LDV, Fernandes AC (2022) Moonpool dimensions and position optimization with Genetic algorithm of a drillship in random seas. Ocean Eng 247:110561. https://doi.org/10.1016/j.oceaneng.2022.110561

    Google Scholar 

  38. Saji Y, Barkatou M (2021) A discrete bat algorithm based on Lévy flights for Euclidean traveling salesman problem. Expert Syst Appl 172:114639. https://doi.org/10.1016/j.eswa.2021.114639

    Google Scholar 

  39. Mirjalili SZ, Mirjalili S, Saremi S et al (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

    Google Scholar 

  40. Ferreira MVDS, Rios R, Mello R, Rios TN (2021) Using fuzzy clustering to address imprecision and uncertainty present in deterministic components of time series. Appl Soft Comput 113:108011. https://doi.org/10.1016/j.asoc.2021.108011

    Google Scholar 

  41. Yang SC, Liu J (2018) Time series forecasting based on high-order fuzzy cognitive maps and wavelet transform. IEEE Trans Fuzzy Syst 26(6):3391–3402

    Google Scholar 

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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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Authors and Affiliations

  1. Key Laboratory of Intelligent Analysis and Decision on Complex Systems, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Sidong Xian

  2. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Sidong Xian, Hualiang Lei & Kaiyuan Chen

  3. Library Office, Sichuan University of Arts and Sciences, Dazhou, 635000, People’s Republic of China

    Zhengyan Li

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  1. Sidong Xian
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  2. Hualiang Lei
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Correspondence to Sidong Xian.

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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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