Skip to main content
SpringerLink
Log in

A hybrid sigma-pi neural network for combined intuitionistic fuzzy time series prediction model

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Intuitionistic fuzzy time series models consider observations hesitation degree but they use memberships and non-membership values together as inputs in the prediction system. The usage of membership and non-membership values as inputs in separate prediction models and combining the outputs of these separate models will provide a more flexible computational approach. Thus, different effects of membership and non-membership degrees on the predictions can be revealed. In this paper, an intuitionistic fuzzy time series prediction model (IFTS-PM) has been proposed. The proposed IFTS-PM uses a new hybrid sigma-pi neural network (HSP-NN), introduced for the first time in the literature, to determine nonlinear relationships between inputs and outputs. In addition, this newly proposed HSP-NN has the ability to multiply linear functions of inputs by unequal weights and convert them to nonlinear relationships. The structure of the proposed IFTS-PM consists of three parts. Two different HSP-NNs generate predictions by taking into account the different contribution levels of memberships and non-memberships. The last part is the part where these predictions are combined. Modified particle swarm optimization is performed to obtain optimal weights of HSP-NNs as well as the combination weights. And by taking the advantage of intuitionistic fuzzy C-means, fuzzy clusters, membership and non-membership values of observations are obtained. Performance of the proposed model is verified by applying it on 48 time series data sets. With all used indications, it has been clearly observed that proposed model has produced outstanding predictions compared to some other state-of-the-art prediction tools.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Egrioglu E, Aladag CH, Yolcu U, Bas E (2015) A new adaptive network based fuzzy inference system for time series forecasting. Aloy J Soft Comput Appl 2:25–32

    Google Scholar 

  2. Cagcag Yolcu O, Alpaslan F (2018) Prediction of TAIEX based on hybrid fuzzy time series model with single optimization process. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2018.02.007

    Article  Google Scholar 

  3. Tak N, Evren AA, Tez M, Egrioglu E (2018) Recurrent type-1 fuzzy functions approach for time series forecasting. Appl Intell. https://doi.org/10.1007/s10489-017-0962-8

    Article  Google Scholar 

  4. Egrioglu E, Bas E, Yolcu OC, Yolcu U (2019) Intuitionistic time series fuzzy inference system. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.03.024

    Article  MATH  Google Scholar 

  5. Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst 54:269–277. https://doi.org/10.1016/0165-0114(93)90372-O

    Article  MathSciNet  MATH  Google Scholar 

  6. Zadeh LA (1965) J (Zadeh) fuzzy sets.pdf. Inf Control 8:338–353

    Article  Google Scholar 

  7. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst. https://doi.org/10.1016/S0165-0114(86)80034-3

    Article  MATH  Google Scholar 

  8. Yadav RN, Kalra PK, John J (2007) Time series prediction with single multiplicative neuron model. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2006.01.003

    Article  Google Scholar 

  9. Chen SM (2002) Forecasting enrollments based on high-order fuzzy time series. Cybern Syst. https://doi.org/10.1080/019697202753306479

    Article  MATH  Google Scholar 

  10. 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. https://doi.org/10.1016/j.eswa.2007.12.013

    Article  Google Scholar 

  11. Chen SM, Chang YC (2010) Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Inf Sci (Ny). https://doi.org/10.1016/j.ins.2010.08.026

    Article  Google Scholar 

  12. Wang L, Liu X, Pedrycz W (2013) Effective intervals determined by information granules to improve forecasting in fuzzy time series. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2013.04.026

    Article  Google Scholar 

  13. Wang L, Liu X, Pedrycz W, Shao Y (2014) Determination of temporal information granules to improve forecasting in fuzzy time series. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2013.10.046

    Article  Google Scholar 

  14. Huarng K (2001) Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. https://doi.org/10.1016/S0165-0114(00)00057-9

    Article  MathSciNet  MATH  Google Scholar 

  15. Aladag CH, Yolcu U, Egrioglu E (2010) A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks. Math Comput Simul. https://doi.org/10.1016/j.matcom.2010.09.011

    Article  MathSciNet  MATH  Google Scholar 

  16. Egrioglu E, Aladag CH, Yolcu U et al (2011) Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2011.02.052

    Article  MATH  Google Scholar 

  17. Huarng K, Yu THK (2006) Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans Syst Man Cybern Part B Cybern. https://doi.org/10.1109/TSMCB.2005.857093

    Article  Google Scholar 

  18. Yolcu U, Egrioglu E, Uslu VR et al (2009) A new approach for determining the length of intervals for fuzzy time series. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2008.09.002

    Article  Google Scholar 

  19. Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst. https://doi.org/10.1016/0165-0114(93)90372-O

    Article  MathSciNet  MATH  Google Scholar 

  20. Lee HS, Chou MT (2004) Fuzzy forecasting based on fuzzy time series. Int J Comput Math. https://doi.org/10.1080/00207160410001712288

    Article  MathSciNet  MATH  Google Scholar 

  21. Lee LW, Wang LH, Chen SM (2007) Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2006.05.015

    Article  Google Scholar 

  22. Lee LW, Wang LH, Chen SM (2008) Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2006.09.007

    Article  Google Scholar 

  23. Kuo IH, Horng SJ, Chen YH et al (2010) Forecasting TAIFEX based on fuzzy time series and particle swarm optimization. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2009.06.102

    Article  Google Scholar 

  24. Bai E, Wong WK, Chu WC et al (2011) A heuristic time-invariant model for fuzzy time series forecasting. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2010.08.059

    Article  Google Scholar 

  25. Chen SM, Chen CD (2011) TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2010.2073712

    Article  Google Scholar 

  26. Gangwar SS, Kumar S (2012) Partitions based computational method for high-order fuzzy time series forecasting. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2012.04.039

    Article  MATH  Google Scholar 

  27. Cheng SH, Chen SM, Jian WS (2016) Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf Sci (NY). https://doi.org/10.1016/j.ins.2015.08.024

    Article  MATH  Google Scholar 

  28. Gep BOX (1953) Non-normality and tests on variances. Biometrika. https://doi.org/10.1093/biomet/40.3-4.318

    Article  MathSciNet  Google Scholar 

  29. Chen SM (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. https://doi.org/10.1016/0165-0114(95)00220-0

    Article  Google Scholar 

  30. Liu HT (2007) An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers. Fuzzy Optim Decis Mak. https://doi.org/10.1007/s10700-006-0025-9

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhao L, Yang Y (2009) PSO-based single multiplicative neuron model for time series prediction. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2008.01.061

    Article  Google Scholar 

  32. Chen MY, Chen BT (2015) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci (Ny). https://doi.org/10.1016/j.ins.2014.09.038

    Article  Google Scholar 

  33. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Springer, Boston

    Book  Google Scholar 

  34. Wei LY, Cheng CH, Wu HH (2014) A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2014.01.022

    Article  Google Scholar 

  35. Cheng SH, Chen SM, Jian WS (2016) A novel fuzzy time series forecasting method based on fuzzy logical relationships and similarity measures. In: Proceedings—2015 IEEE international conference on systems, man, and cybernetics, SMC 2015

  36. Li ST, Cheng YC (2010) A stochastic HMM-based forecasting model for fuzzy time series. IEEE Trans Syst Man Cybern Part B Cybern. https://doi.org/10.1109/TSMCB.2009.2036860

    Article  Google Scholar 

  37. 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 J. https://doi.org/10.1016/j.asoc.2012.05.002

    Article  Google Scholar 

  38. Chen SM, Chu HP, Sheu TW (2012) TAIEX forecasting using fuzzy time series and automatically generated weights of multiple factors. IEEE Trans Syst Man, Cybern Part ASystems Humans. https://doi.org/10.1109/TSMCA.2012.2190399

    Article  Google Scholar 

  39. Cheng YC, Li ST (2012) Fuzzy time series forecasting with a probabilistic smoothing hidden Markov model. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2011.2173583

    Article  Google Scholar 

  40. Eǧrioǧlu E (2012) A new time-invariant fuzzy time series forecasting method based on genetic algorithm. Adv Fuzzy Syst. https://doi.org/10.1155/2012/785709

    Article  MathSciNet  Google Scholar 

  41. Dos Santos FJJ, De Arruda Camargo H (2013) Preprocessing in fuzzy time series to improve the forecasting accuracy. In: Proceedings—2013 12th international conference on machine learning and applications, ICMLA 2013

  42. Yolcu U, Aladag CH, Egrioglu E, Uslu VR (2013) Time-series forecasting with a novel fuzzy time-series approach: an example for Istanbul stock market. J Stat Comput Simul. https://doi.org/10.1080/00949655.2011.630000

    Article  MathSciNet  MATH  Google Scholar 

  43. Aladag CH, Egrioglu E, Yolcu U (2014) Robust multilayer neural network based on median neuron model. Neural Comput Appl. https://doi.org/10.1007/s00521-012-1315-5

    Article  MATH  Google Scholar 

  44. Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst. https://doi.org/10.1016/0165-0114(93)90355-L

    Article  Google Scholar 

  45. Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series—part II. Fuzzy Sets Syst. https://doi.org/10.1016/0165-0114(94)90067-1

    Article  Google Scholar 

  46. Sullivan J, Woodall WH (1994) A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets Syst. https://doi.org/10.1016/0165-0114(94)90152-X

    Article  Google Scholar 

  47. Huarng KH, Yu THK, Hsu YW (2007) A multivariate heuristic model for fuzzy time-series forecasting. IEEE Trans Syst Man, Cybern Part B Cybern. https://doi.org/10.1109/TSMCB.2006.890303

    Article  Google Scholar 

  48. Chen SM, Chen SW (2015) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2014.2326888

    Article  Google Scholar 

  49. Aladag CH, Basaran MA, Egrioglu E et al (2009) Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2008.04.001

    Article  Google Scholar 

  50. Egrioglu E, Aladag CH, Yolcu U et al (2009) A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2009.02.057

    Article  MATH  Google Scholar 

  51. Egrioglu E, Aladag CH, Yolcu U et al (2009) A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2008.09.040

    Article  MATH  Google Scholar 

  52. Egrioglu E (2014) PSO-based high order time invariant fuzzy time series method: application to stock exchange data. Econ Model. https://doi.org/10.1016/j.econmod.2014.02.017

    Article  Google Scholar 

  53. Wang J, Xiong S (2014) A hybrid forecasting model based on outlier detection and fuzzy time series—a case study on Hainan wind farm of China. Energy. https://doi.org/10.1016/j.energy.2014.08.064

    Article  Google Scholar 

  54. Lee WJ, Hong J (2015) A hybrid dynamic and fuzzy time series model for mid-term power load forecasting. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2014.08.006

    Article  Google Scholar 

  55. Aladag CH (2013) Using multiplicative neuron model to establish fuzzy logic relationships. Expert Syst Appl 40:850–853

    Article  Google Scholar 

  56. Faruk ALPASLANOC (2012) A seasonal fuzzy time series forecasting method based On Gustafson Kessel fuzzy clustering. J Soc Econ Stat 1:1–13

    Google Scholar 

  57. Alpaslan F, Cagcag O, Aladag CH et al (2012) A novel seasonal fuzzy time series method. Hacettepe J Math Stat 41:375–385

    MathSciNet  MATH  Google Scholar 

  58. Bas E, Egrioglu E, Aladag CH, Yolcu U (2015) Fuzzy-time-series network used to forecast linear and nonlinear time series. Appl Intell. https://doi.org/10.1007/s10489-015-0647-0

    Article  Google Scholar 

  59. Cheng CH, Chang JR, Yeh CA (2006) Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2005.07.004

    Article  Google Scholar 

  60. Chen S, Hsu C (2004) A new method to forecast enrollments using fuzzy time series. Int J Appl Sci Eng. https://doi.org/10.6703/IJASE.2004.2(3).234

    Article  Google Scholar 

  61. Chen SM, Wang NY, Pan JS (2009) Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2009.02.085

    Article  Google Scholar 

  62. Kocak C (2017) ARMA(p, q) type high order fuzzy time series forecast method based on fuzzy logic relations. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2017.04.021

    Article  Google Scholar 

  63. Şişman-Yilmaz NA, Alpaslan FN, Jain L (2004) ANFIS_unfolded_in_time for multivariate time series forecasting. Neurocomputing. https://doi.org/10.1016/j.neucom.2004.03.009

    Article  Google Scholar 

  64. Firat M, Güngör M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul. https://doi.org/10.1016/j.matcom.2006.09.003

    Article  MathSciNet  MATH  Google Scholar 

  65. Zanaganeh M, Mousavi SJ, Etemad Shahidi AF (2009) A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2009.04.009

    Article  Google Scholar 

  66. Khashei M, Bijari M, Raissi Ardali GA (2009) Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neurocomputing. https://doi.org/10.1016/j.neucom.2008.04.017

    Article  Google Scholar 

  67. Azadeh A, Asadzadeh SM, Ghanbari A (2010) An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments. Energy Policy. https://doi.org/10.1016/j.enpol.2009.11.036

    Article  Google Scholar 

  68. Azadeh A, Asadzadeh SM, Saberi M et al (2011) A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE. Appl Energy. https://doi.org/10.1016/j.apenergy.2011.04.027

    Article  Google Scholar 

  69. Li K, Su H, Chu J (2011) Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: a comparative study. Energy Build. https://doi.org/10.1016/j.enbuild.2011.07.010

    Article  Google Scholar 

  70. Chang JR, Wei LY, Cheng CH (2011) A hybrid ANFIS model based on AR and volatility for TAIEX forecasting. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2010.04.010

    Article  Google Scholar 

  71. Pousinho HMI, Mendes VMF, Catalão JPS (2012) Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2012.01.001

    Article  Google Scholar 

  72. Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. Int J Electr Power Energy Syst. https://doi.org/10.1016/j.ijepes.2016.03.012

    Article  Google Scholar 

  73. Inyurt S, Ghaffari Razin MR (2021) Regional application of ANFIS in ionosphere time series prediction at severe solar activity period. Acta Astronaut 179:450–461. https://doi.org/10.1016/J.ACTAASTRO.2020.11.027

    Article  Google Scholar 

  74. Arora S, Keshari AK (2021) ANFIS-ARIMA modelling for scheming re-aeration of hydrologically altered rivers. J Hydrol 601:126635. https://doi.org/10.1016/J.JHYDROL.2021.126635

    Article  Google Scholar 

  75. Zardkoohi M, Fatemeh Molaeezadeh S (2022) Long-term prediction of blood pressure time series using ANFIS system based on DKFCM clustering. Biomed Signal Process Control 74:103480. https://doi.org/10.1016/J.BSPC.2022.103480

    Article  Google Scholar 

  76. Hussain W, Merigó JM, Raza MR, Gao H (2022) A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning. Inf Sci (Ny) 584:280–300. https://doi.org/10.1016/J.INS.2021.10.054

    Article  Google Scholar 

  77. Wang Y, Lei Y, Fan X, Wang Y (2016) Intuitionistic fuzzy time series forecasting model based on intuitionistic fuzzy reasoning. Math Probl Eng. https://doi.org/10.1155/2016/5035160

    Article  MathSciNet  MATH  Google Scholar 

  78. Cagcag Yolcu O, Bas E, Egrioglu E, Yolcu U (2020) A new intuitionistic fuzzy functions approach based on hesitation margin for time-series prediction. Soft Comput. https://doi.org/10.1007/s00500-019-04432-2

    Article  MATH  Google Scholar 

  79. Kizilaslan B, Egrioglu E, Evren AA (2020) Intuitionistic fuzzy ridge regression functions. Commun Stat Simul Comput. https://doi.org/10.1080/03610918.2019.1626887

    Article  MathSciNet  MATH  Google Scholar 

  80. Fan X, Wang Y, Zhang M (2020) Network traffic forecasting model based on long-term intuitionistic fuzzy time series. Inf Sci (Ny). https://doi.org/10.1016/j.ins.2019.08.023

    Article  Google Scholar 

  81. Egrioglu E, Yolcu U, Bas E (2019) Intuitionistic high-order fuzzy time series forecasting method based on pi-sigma artificial neural networks trained by artificial bee colony. Granul Comput 4:639–654. https://doi.org/10.1007/s41066-018-00143-5

    Article  Google Scholar 

  82. Bas E, Yolcu U, Egrioglu E (2021) Intuitionistic fuzzy time series functions approach for time series forecasting. Granul Comput. https://doi.org/10.1007/s41066-020-00220-8

    Article  MATH  Google Scholar 

  83. Dong Q, Ma X (2021) Enhanced fuzzy time series forecasting model based on hesitant differential fuzzy sets and error learning. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.114056

    Article  Google Scholar 

  84. Zadeh LA (1965) Fuzzy sets. Inf Control. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  MATH  Google Scholar 

  85. Chaira T (2011) A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Appl Soft Comput J 11:1711–1717

    Article  Google Scholar 

  86. Geurts M, Box GEP, Jenkins GM (1977) Time series analysis: forecasting and control. J Mark Res. https://doi.org/10.2307/3150485

    Article  Google Scholar 

  87. Brown RG (1957) Exponential smoothing for predicting demand. Oper Res 5:145

    Article  Google Scholar 

  88. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature. https://doi.org/10.1038/323533a0

    Article  MATH  Google Scholar 

  89. Türkşen IB (2008) Fuzzy functions with LSE. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2007.12.004

    Article  Google Scholar 

  90. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  91. Sarıca B, Eğrioğlu E, Aşıkgil B (2018) A new hybrid method for time series forecasting: AR–ANFIS. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2475-5

    Article  Google Scholar 

  92. Hsu LY, Horng SJ, Kao TW et al (2010) Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2009.09.015

    Article  Google Scholar 

  93. Yu THK, Huarng KH (2008) A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2007.05.016

    Article  Google Scholar 

  94. Chen YS, Cheng CH, Tsai WL (2014) Modeling fitting-function-based fuzzy time series patterns for evolving stock index forecasting. Appl Intell. https://doi.org/10.1007/s10489-014-0520-6

    Article  Google Scholar 

  95. Chen SM, Manalu GMT, 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. https://doi.org/10.1109/TSMCB.2012.2223815

    Article  Google Scholar 

  96. Egrioglu E, Aladag CH, Yolcu U et al (2010) Finding an optimal interval length in high order fuzzy time series. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2009.12.006

    Article  MATH  Google Scholar 

  97. Cai Q, Zhang D, Zheng W, Leung SCH (2015) A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2014.11.003

    Article  Google Scholar 

  98. Chen YS, Cheng CH, Chiu CL, Huang ST (2016) A study of ANFIS-based multi-factor time series models for forecasting stock index. Appl Intell 45:277–292. https://doi.org/10.1007/s10489-016-0760-8

    Article  Google Scholar 

  99. Wei LY (2016) A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2016.01.027

    Article  Google Scholar 

  100. Su CH, Cheng CH (2016) A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock. Neurocomputing. https://doi.org/10.1016/j.neucom.2016.03.068

    Article  Google Scholar 

  101. Sadaei HJ, Enayatifar R, Lee MH, Mahmud M (2016) A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2015.11.026

    Article  Google Scholar 

  102. Huarng K (2001) Heuristic models of fuzzy time series for forecasting. Fuzzy Sets Syst. https://doi.org/10.1016/S0165-0114(00)00093-2

    Article  MathSciNet  MATH  Google Scholar 

  103. Yu HK (2005) Weighted fuzzy time series models for TAIEX forecasting. Phys A Stat Mech Appl. https://doi.org/10.1016/j.physa.2004.11.006

    Article  Google Scholar 

  104. Chen SM, Tanuwijaya K (2011) Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2011.06.019

    Article  Google Scholar 

  105. Aladag CH, Yolcu U, Egrioglu E, Bas E (2014) Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2014.03.028

    Article  Google Scholar 

  106. Askari S, Montazerin N, Zarandi MHF (2015) A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2015.06.028

    Article  Google Scholar 

  107. Cagcag Yolcu O, Yolcu U, Egrioglu E, Aladag CH (2016) High order fuzzy time series forecasting method based on an intersection operation. Appl Math Model. https://doi.org/10.1016/j.apm.2016.05.012

    Article  MathSciNet  MATH  Google Scholar 

  108. Ye F, Zhang L, Zhang D et al (2016) A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inf Sci (NY). https://doi.org/10.1016/j.ins.2016.05.038

    Article  MATH  Google Scholar 

  109. Wan Y, Si YW (2017) Adaptive neuro fuzzy inference system for chart pattern matching in financial time series. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2017.03.023

    Article  Google Scholar 

  110. Cheng CH, Yang JH (2018) Fuzzy time-series model based on rough set rule induction for forecasting stock price. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.04.014

    Article  Google Scholar 

  111. Wu H, Long H, Jiang J (2019) Handling forecasting problems based on fuzzy time series model and model error learning. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2019.02.021

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, Giresun University, Giresun, Turkey

    Sule Nazlı Arslan

  2. Department of Statistics, Marmara University, Istanbul, Turkey

    Ozge Cagcag Yolcu

Authors
  1. Sule Nazlı Arslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ozge Cagcag Yolcu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ozge Cagcag Yolcu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arslan, S.N., Cagcag Yolcu, O. A hybrid sigma-pi neural network for combined intuitionistic fuzzy time series prediction model. Neural Comput & Applic 34, 12895–12917 (2022). https://doi.org/10.1007/s00521-022-07138-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07138-z

Keywords

Search

Navigation