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Integrate new cross association fuzzy logical relationships to multi-factor high-order forecasting model of time series

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Abstract

In any multi-factor high-order fuzzy logical relationship (FLR) based forecasting model, a FLR reflects the influence of both the main factor (the forecasted factor) and all the influence factors on the main factor. Thus, the antecedent of a FLR includes multiple premises related to the main factor as well as all the influence factors. In real time series, there may exist another kind of influence: the cross association influence which is from a part of influence factor(s) on the main factor. To describe such kind of influence, we propose the concept of multi-factor high-order cross association FLRs (CAFLRs). The antecedent of a CAFLR includes some premises related to a part of influence factors. The proposed CAFLRs are divided into two categories: short-cross association FLRs and long-cross association FLRs, which describe the influence on the consequent observation from the premise observations at the closest consecutive moments and the premise observations at the non-closest non-consecutive moments respectively. Based on the concept of CAFLRs, a novel forecasting model is built up. In the proposed model, more FLRs than in the existing models can be mined from historical observations and added to the rule base, which further improve the prediction accuracy by raising the possibility of finding available forecasting FLRs. Superior performance of the proposed model has been verified in the experiments by comparing with Nonlinear Autoregressive Neural Networks, Autoregressive Model, Support Vector Regression and some other FLR based forecasting models.

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References

  1. Chen SM, Chun NY (2006) Forecast enrollments using high-order fuzzy time series and genetic algorithms. Int J Intelligence Syst 21(5):485–501

    Article  MathSciNet  Google Scholar 

  2. Chen SM, IEEE, Hwang JR (2000) Temperature prediction using fuzzy time series. IEEE Trans Syst Man Cybern. B 30(2):263-275un

  3. Garg B, Beg MMS, Ansari AQ (2013) Fuzzy time series model to forecast rice production. In: 2013 IEEE international conference on fuzzy systems

  4. Huarng KH, Yu HK (2005) A type 2 fuzzy time series model for stock index forecasting. Phys A 353:445–462

    Article  Google Scholar 

  5. Wang JZ, Li HM, Lu HY (2018) Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China. Appl Soft Comput 71:783–799

  6. Chen CS, Jhong YD, Wu WZ, Chen ST (2019) Fuzzy time series for real-time flood forecasting. Stoch Env Res Risk Assess 33(3):645–656

    Article  Google Scholar 

  7. Huarng KH (2001) Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst 123(3):387–394

    Article  MathSciNet  Google Scholar 

  8. Wang LZ, Liu XD, Pedrycz W (2013) Effective intervals determined by information granules to improve forecasting in fuzzy time series. Expert Syst Appl 40(14):5673–5679

    Article  Google Scholar 

  9. Amjad U, Jilani TA, Yasmeen F (2012) A two phase algorithm for fuzzy time series forecasting using genetic algorithm and particle swarm optimization techniques. Int J Comput Vision 55(16):34–40

    Google Scholar 

  10. Cai QS, Zhang DF, Zheng W, Leung SCH (2015) A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowl-Based Syst 74:61–68

    Article  Google Scholar 

  11. Cai QS, Zhang DF, Wu B, Leung SCH (2013) A novel stock forecasting model based on fuzzy time series and genetic algorithm. Process Comput Sci 18:1155–1162

    Article  Google Scholar 

  12. Sakhuja S, Jain V, Kumar S, Chandra C, Ghildayal SK (2015) Genetic algorithm based fuzzy time series tourism demand forecast model. Ind Manag Data Syst 116(3):483–507

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Song Q, Chissom BS (1991) Forecasting enrollments with fuzzy time series: Part I. The Annual Meeting of the Mid-South Educational Research Association, Lexington. KY

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Abhishekh GSS, Singh SR (2018) A refined method of forecasting based on high-order intuitionistic fuzzy time series data. Process Artificial Intell 7(4):339–350

    Article  Google Scholar 

  18. Bas E, Grosan G, Egrioglu E, Yolcu U (2018) High-order fuzzy time series method based on pi-sigma neural network. Eng Appl Artif Intell 72:350–356

    Article  Google Scholar 

  19. Kumar S, Gangwar SS (2015) Intuitionistic fuzzy time series: an approach for handling nondeterminism in time series forecasting. IEEE Trans Fuzzy Syst 24(6):1270–1281

    Article  Google Scholar 

  20. Chen SM (2002) Forecasting enrollments based on high-order fuzzy time series. Fuzzy Sets Syst 33:1–16

    MATH  Google Scholar 

  21. Bose M, Mali K (2018) A novel data partitioning and rule selection technique for modeling high-order fuzzy time series. Appl Soft Comput 63:87–96

    Article  Google Scholar 

  22. Lee LW, Wang LH, Chen SM, Leu YH (2006) Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Trans Fuzzy Syst 14(3):468–477

    Article  Google Scholar 

  23. Yu THK, Huarng KH (2008) A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst Appl 34(4):2945–2952

    Article  Google Scholar 

  24. Chen SM, Chang YC (2010) Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Inf Sci 180(24):4772–4783

    Article  MathSciNet  Google Scholar 

  25. Yu HK (2005) Weighted fuzzy time series models for TAIEX forecasting. Phys A 349(3–4):609–624

    Article  Google Scholar 

  26. Teoh HJ, Chen TL, Cheng CH (2007) Frequency-weighted fuzzy time series based on Fibonacci sequence for TAIEX forecasting. Emerging Technologies in Knowledge Discovery and Data Mining pp 27–34

  27. Qiu WR, Liu XD, Li HL (2011) A generalized method for forecasting based on fuzzy time series. Expert Syst Appl 38:10446–10453

    Article  Google Scholar 

  28. Jiang P, Dong QL, Li PZ, Lian LL (2017) A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction. Appl Soft Comput 55:44–62

    Article  Google Scholar 

  29. Bisht KL, Kumar SJ (2016) Fuzzy time series forecasting method based on hesitant fuzzy sets. Expert Syst Appl 64:557–568

    Article  Google Scholar 

  30. Cheng SH, Chen SM, Jian WS (2015) A novel fuzzy time series forecasting method based on fuzzy logical relationships and similarity measures. In: 2015 IEEE international conference on systems pp 2250–2254

  31. Daniel OA, Jens RP (2018) A weighted fuzzy time series forecasting model. Indian J Sci Technol 11(27)

  32. Lertworaprachaya Y, Yang YJ, John R (2010) High-order type-2 fuzzy time series. In: 2010 International conference of soft computing and pattern recognition pp 363–368

  33. Sulandari W, Yudhanto Y (2015) Forecasting trend data using a hybrid simple moving average-weighted fuzzy time series model. In: 2015 international conference on science in information technology

  34. Ye F, Zhang LM, Zhang DF, Fujita H, Gong ZG (2016) A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inf Sci 367–368:41–57

    Article  Google Scholar 

  35. Singh P (2017) A brief review of modeling approaches based on fuzzy time series. Int J Mach Learn & Cyber 8:397–420

    Article  Google Scholar 

  36. Zadeh LA (1965) Fuzzy sets. Information and Control pp 338–353

  37. Chen SM, Chen SW (2015) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the possibilities of trends of fuzzy logical relationships. IEEE Trans Cybern 45(3):405–417

    Google Scholar 

  38. Chen SM, Manalu GMTM, Pan JS, Liu HC (2013) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Trans Cybern 43(3):1102–1117

    Article  Google Scholar 

  39. Wang NY, Chen SM (2009) Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series. Expert Syst Appl 36(2):2143–2154

    Article  Google Scholar 

  40. Li F, Yu FS (2018) A long-association relationship based forecasting method for time series. In: 14th int conf on natural computation, fuzzy systems and knowledge discovery pp 548–554

  41. Abhishekh GSS, Singh SR (2017) A refined weighted method for forecasting based on type 2 fuzzy time series. Int J Model Simul 38(3):180–188

    Article  Google Scholar 

  42. Lu W, Chen XY, Pedrycz W, Liu XD, Yang JH (2015) Using interval information granules to improve forecasting in fuzzy time series. Int J Approx Reason 57:1–18

    Article  Google Scholar 

  43. Qiu WR, Liu XD, Li HL (2013) High-order fuzzy time series model based on generalized fuzzy logical relationship. Math Probl Eng 1:1–11

    MathSciNet  MATH  Google Scholar 

  44. Singh P, Dhiman G (2018) A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. J Comput Sci-Neth 27:370–385

    Article  Google Scholar 

  45. Aladag CH, Yolcu U, Egrioglu E, Dalar AZ (2012) A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Appl Soft Comput 12:3291–3299

    Article  Google Scholar 

  46. Aladag CH, Egrioglu E, Yolcu U (2014) A high order seasonal fuzzy time series model and application to international tourism demand of turkey. J Intell Fuzzy Syst 26:295–302

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 11971065, No. 61966039), Key Laboratory of Intelligent Computing and Information Processing (Fujian Province University), and Fujian Provincial Big Data Research Institute of Intelligent Manufacturing.

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Authors and Affiliations

  1. Department of Mathematics, College of Arts and Sciences, Shanghai Maritime University, Shanghai, 201306, China

    Fang Li

  2. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, 363000, Fujian, China

    Fusheng Yu

  3. School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, China

    Fusheng Yu & Yuming Liu

  4. School of Economics and Management, Beijing Institute of Petrochemical Technology, Beijing, 102617, China

    Xiao Wang

  5. Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou Normal University, Quanzhou, 362000, China

    Xiyang Yang

  6. School of Mathematics and Computer Sciences, Yunnan Minzu University, Kunming, 650500, China

    Shihu Liu

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  1. Fang Li
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  2. Fusheng Yu
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  3. Xiao Wang
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  4. Xiyang Yang
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  6. Yuming Liu
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Correspondence to Fusheng Yu.

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Li, F., Yu, F., Wang, X. et al. Integrate new cross association fuzzy logical relationships to multi-factor high-order forecasting model of time series. Int. J. Mach. Learn. & Cyber. 12, 2297–2315 (2021). https://doi.org/10.1007/s13042-021-01310-y

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  • DOI: https://doi.org/10.1007/s13042-021-01310-y

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