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A new intuitionistic fuzzy functions approach based on hesitation margin for time-series prediction

  • Ozge Cagcag YolcuEmail author
  • Eren Bas
  • Erol Egrioglu
  • Ufuk Yolcu
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Abstract

There are various studies in which a variety of prediction tools have been introduced in time-series prediction literature. Non-probabilistic approaches which are based on fuzzy set theory, especially in recent years, have been put forward. Although these approaches including adaptive network fuzzy inference system, fuzzy functions approach, and fuzzy regression can be successfully utilized as a prediction tool, they have not been designed for prediction problem and they pass over the dependency structure of time-series observations. From this point forth, designing a prediction tool that considers the dependency structure of the observations of time series will procure to get predictions more accurately. Although the membership values, in the analysis process, are taken into account in almost all fuzzy methods, the non-membership and hesitation values are not considered. However, using as much information as possible on time series may be another positive factor that gives more accurate predictions. The primary aim of this study, for time-series prediction, is to introduce an intuitionistic fuzzy regression functions approach based on hesitation margin (I-FRF-HM). In the introduced intuitionistic fuzzy regression functions approach, two inference systems are separately constituted such that while one of them uses membership, other one uses non-membership values as inputs of inference system in addition with the crisp observations of time series. Predictions obtained from each system are converted into final predictions of whole inference system via an approach based on hesitation margin. Intuitionistic fuzzy C-means are utilized to get membership and non-membership values in the proposed model. The proposed I-FRF-HM has been applied to various real-world time series. The obtained findings are evaluated along with the results of some other time-series prediction models. The results show that the proposed I-FRF-HM has superior prediction performance to other prediction models.

Keywords

Time-series prediction Fuzzy regression functions Intuitionistic fuzzy C-means Hesitation margin 

Notes

Acknowledgements

This study is carried out by using facilities of Giresun University Forecast Research Laboratory http://forelab.giresun.edu.tr.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this work.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Abbasi M, El Hanandeh A (2016) Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag 56:13–22.  https://doi.org/10.1016/j.wasman.2016.05.018 CrossRefGoogle Scholar
  2. Aladag CH, Yolcu U, Egrioglu E (2010a) 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 MathSciNetCrossRefzbMATHGoogle Scholar
  3. Aladag CH, Yolcu U, Egrioglu E (2010b) A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks. Math Comput Simul 81:875–882.  https://doi.org/10.1016/j.matcom.2010.09.011 MathSciNetCrossRefzbMATHGoogle Scholar
  4. Aladag S, Aladag CH, Mentes T, Egrioglu E (2012) A new seasonal fuzzy time series method based on the multiplicative neuron model and SARIMA. Hacettepe J Math, Stat, p 41zbMATHGoogle Scholar
  5. Alpaslan F, Cagcag O (2012) A seasonal fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering. J Soc Econ Stat 1:1–13Google Scholar
  6. Askari S, Montazerin N (2015) A high-order multi-variable Fuzzy Time Series forecasting algorithm based on fuzzy clustering. Expert Syst Appl 42:2121–2135CrossRefGoogle Scholar
  7. 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 35:151–160.  https://doi.org/10.1016/j.asoc.2015.06.028 CrossRefGoogle Scholar
  8. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96CrossRefGoogle Scholar
  9. Atsalakis GS, Valavanis KP (2009) Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert Syst Appl 36:10696–10707.  https://doi.org/10.1016/j.eswa.2009.02.043 CrossRefGoogle Scholar
  10. 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 38:1529–1536.  https://doi.org/10.1016/j.enpol.2009.11.036 CrossRefGoogle Scholar
  11. Azadeh A, Asadzadeh SM, Saberi M et al (2011a) 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 88:3850–3859.  https://doi.org/10.1016/j.apenergy.2011.04.027 CrossRefGoogle Scholar
  12. Azadeh A, Saberi M, Asadzadeh SM (2011b) An adaptive network based fuzzy inference system–auto regression–analysis of variance algorithm for improvement of oil consumption estimation and policy making: the cases of Canada, United Kingdom, and South Korea. Appl Math Model 35:581–593.  https://doi.org/10.1016/j.apm.2010.06.001 CrossRefGoogle Scholar
  13. Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm. Int J Electr Power Energy Syst 82:92–104.  https://doi.org/10.1016/j.ijepes.2016.03.012 CrossRefGoogle Scholar
  14. 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 CrossRefGoogle Scholar
  15. Cai Q, Zhang D, Zheng W, Leung SCH (2015) A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowledge-Based Syst 74:61–68.  https://doi.org/10.1016/j.knosys.2014.11.003 CrossRefGoogle Scholar
  16. Celikyilmaz A, Turksen IB (2009) Modeling uncertainty with fuzzy logic. Stud Fuzziness Soft Comput 240:149–215CrossRefGoogle Scholar
  17. Chaira T (2011) A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Appl Soft Comput 11:1711–1717.  https://doi.org/10.1016/j.asoc.2010.05.005 CrossRefGoogle Scholar
  18. Chang F-J, Chang Y-T (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29:1–10.  https://doi.org/10.1016/j.advwatres.2005.04.015 CrossRefGoogle Scholar
  19. Chang BR, Tsai HF (2009) Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis. Appl Soft Comput 9:1177–1183.  https://doi.org/10.1016/j.asoc.2009.03.003 CrossRefGoogle Scholar
  20. Chang J-R, Wei L-Y, Cheng C-H (2011) A hybrid ANFIS model based on AR and volatility for TAIEX forecasting. Appl Soft Comput 11:1388–1395.  https://doi.org/10.1016/j.asoc.2010.04.010 CrossRefGoogle Scholar
  21. Chen S-M (1996) Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst 81:311–319.  https://doi.org/10.1016/0165-0114(95)00220-0 CrossRefGoogle Scholar
  22. Chen S-M (2002) Forecasting enrollments based on high-order fuzzy time series. Cybern Syst 33:1–16CrossRefGoogle Scholar
  23. Chen M-Y (2014) A high-order fuzzy time series forecasting model for internet stock trading. Futur Gener Comput Syst 37:461–467.  https://doi.org/10.1016/j.future.2013.09.025 CrossRefGoogle Scholar
  24. Chen SM, Chang YC (2010) Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques. Inf Sci (Ny) 180:4772–4783MathSciNetCrossRefGoogle Scholar
  25. Chen SM, Chen CD (2011) TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans Fuzzy Syst 19:1–12CrossRefGoogle Scholar
  26. Chen M-Y, Chen B-T (2014) Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform. Appl Soft Comput 14:156–166.  https://doi.org/10.1016/j.asoc.2013.07.024 CrossRefGoogle Scholar
  27. 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 45:405–417Google Scholar
  28. Chen S-M, Chung N-Y (2006) Forecasting enrollments using high-order fuzzy time series and genetic algorithms. Int J Intell Syst 21:485–501.  https://doi.org/10.1002/int.20145 CrossRefzbMATHGoogle Scholar
  29. 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 A Syst Humans 42:1485–1495CrossRefGoogle Scholar
  30. Chen S-M, Manalu GMT, Pan J-S, Liu H-C (2013) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Trans Cybern 43:1102–1117.  https://doi.org/10.1109/TSMCB.2012.2223815 CrossRefGoogle Scholar
  31. Cheng C, Cheng G, Wang J (2008) Multi-attribute fuzzy time series method based on fuzzy clustering. Expert Syst Appl 34:1235–1242.  https://doi.org/10.1016/j.eswa.2006.12.013 CrossRefGoogle Scholar
  32. Cheng C-H, Wei L-Y, Chen Y-S (2009) Fusion ANFIS models based on multi-stock volatility causality for TAIEX forecasting. Neurocomputing 72:3462–3468.  https://doi.org/10.1016/j.neucom.2008.09.027 CrossRefGoogle Scholar
  33. Cheng S-H, Chen S-M, Jian W-S (2016) Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf Sci (Ny) 327:272–287MathSciNetCrossRefGoogle Scholar
  34. Chien S-C, Wang T-Y, Lin S-L (2010) Application of neuro-fuzzy networks to forecast innovation performance: the example of Taiwanese manufacturing industry. Expert Syst Appl 37:1086–1095.  https://doi.org/10.1016/j.eswa.2009.06.107 CrossRefGoogle Scholar
  35. Cobaner M, Citakoglu H, Kisi O, Haktanir T (2014) Estimation of mean monthly air temperatures in Turkey. Comput Electron Agric 109:71–79.  https://doi.org/10.1016/j.compag.2014.09.007 CrossRefGoogle Scholar
  36. Davari S, Zarandi MHF, Turksen IB (2009) An improved fuzzy time series forecasting model based on particle swarm intervalization. In: The 28th North American Fuzzy Information Processing Society Annual Conference (NAFIPS2009). Cincinnati, Ohio, pp 1–5Google Scholar
  37. Egrioglu E (2012) A new time-invariant fuzzy time series forecasting method based on genetic algorithm. Adv Fuzzy Syst 2012:1–6.  https://doi.org/10.1155/2012/785709 MathSciNetCrossRefzbMATHGoogle Scholar
  38. 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 CrossRefzbMATHGoogle Scholar
  39. Egrioglu E, Aladag CH, Basaran MA et al (2011) A new approach based on the optimization of the length of intervals in fuzzy time series. J Intell Fuzzy Syst.  https://doi.org/10.3233/IFS-2010-0470 CrossRefzbMATHGoogle Scholar
  40. Egrioglu E, Aladag CH, Yolcu U (2013) Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst Appl.  https://doi.org/10.1016/j.eswa.2012.05.040 CrossRefzbMATHGoogle Scholar
  41. Egrioglu E, Aladag CH, Yolcu U, Bas E (2014) A new adaptive network based fuzzy inference system for time series forecasting. Aloy J Soft Comput Appl 2:25–32Google Scholar
  42. Firat M, Güngör M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul 75:87–96.  https://doi.org/10.1016/j.matcom.2006.09.003 MathSciNetCrossRefzbMATHGoogle Scholar
  43. Firat M, Turan ME, Yurdusev MA (2009) Comparative analysis of fuzzy inference systems for water consumption time series prediction. J Hydrol 374:235–241.  https://doi.org/10.1016/j.jhydrol.2009.06.013 CrossRefGoogle Scholar
  44. Hooshmand R-A, Amooshahi H, Parastegari M (2013) A hybrid intelligent algorithm based short-term load forecasting approach. Int J Electr Power Energy Syst 45:313–324.  https://doi.org/10.1016/j.ijepes.2012.09.002 CrossRefGoogle Scholar
  45. Hsu LY, Horng SJ, Kao TW et al (2010) Temperature prediction and TAIFEX forecasting based on fuzzy relationships and MTPSO techniques. Expert Syst Appl 37:2756–2770CrossRefGoogle Scholar
  46. Huarng K-H (2001a) Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst 123:387–394.  https://doi.org/10.1016/S0165-0114(00)00057-9 MathSciNetCrossRefzbMATHGoogle Scholar
  47. Huarng K-H (2001b) Heuristic models of fuzzy time series for forecasting. Fuzzy Sets Syst 123:369–386MathSciNetCrossRefGoogle Scholar
  48. Huarng K-H, Yu TH-K (2006) Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans Syst Man Cybern Part B Cybern 36:328–340CrossRefGoogle Scholar
  49. Huarng K-H, Yu THK, Hsu YW (2007) A multivariate heuristic model for fuzzy time-series forecasting. IEEE Trans Syst Man Cybern Part B Cybern 37:836–846CrossRefGoogle Scholar
  50. Park JIl, Lee DJ, Song CK, Chun MG (2010) TAIFEX and KOSPI 200 forecasting based on two-factors high-order fuzzy time series and particle swarm optimization. Expert Syst Appl 37:959–967CrossRefGoogle Scholar
  51. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  52. Karimi S, Kisi O, Shiri J, Makarynskyy O (2013) Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Comput Geosci 52:50–59.  https://doi.org/10.1016/j.cageo.2012.09.015 CrossRefGoogle Scholar
  53. 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 72:956–967.  https://doi.org/10.1016/j.neucom.2008.04.017 CrossRefGoogle Scholar
  54. Kisi O, Shiri J, Nikoofar B (2012) Forecasting daily lake levels using artificial intelligence approaches. Comput Geosci 41:169–180.  https://doi.org/10.1016/j.cageo.2011.08.027 CrossRefGoogle Scholar
  55. Kuo IH, Horng SJ, Kao TW et al (2009) An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Syst Appl 36:6108–6117CrossRefGoogle Scholar
  56. Kuo I-H, Horng S-J, Chen Y-H et al (2010) Forecasting TAIFEX based on fuzzy time series and particle swarm optimization. Expert Syst Appl 37:1494–1502.  https://doi.org/10.1016/j.eswa.2009.06.102 CrossRefGoogle Scholar
  57. Laouafi A, Mordjaoui M, Laouafi F, Boukelia TE (2016) Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology. Int J Electr Power Energy Syst 77:136–144.  https://doi.org/10.1016/j.ijepes.2015.11.046 CrossRefGoogle Scholar
  58. Lee LW, Wang L, Chen S (2007) Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Syst Appl 33:539–550.  https://doi.org/10.1016/j.eswa.2006.05.015 CrossRefGoogle Scholar
  59. 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 34:328–336CrossRefGoogle Scholar
  60. Li S-T, Cheng Y-C, Lin S-Y (2008) A FCM-based deterministic forecasting model for fuzzy time series. Comput Math Appl 56:3052–3063.  https://doi.org/10.1016/j.camwa.2008.07.033 MathSciNetCrossRefzbMATHGoogle Scholar
  61. Li K, Su H, Chu J (2011) Forecasting building energy consumption using neural networks and hybrid neuro-fuzzy system: a comparative study. Energy Build 43:2893–2899.  https://doi.org/10.1016/j.enbuild.2011.07.010 CrossRefGoogle Scholar
  62. Liu J, Wang X, Lu Y (2016) A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system. Renew Energy.  https://doi.org/10.1016/j.renene.2016.10.074 CrossRefGoogle Scholar
  63. Lu W, Chen X, Pedrycz W et al (2015) Using interval information granules to improve forecasting in fuzzy time series. Int J Approx Reason 57:1–18CrossRefGoogle Scholar
  64. Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc Inst Electr Eng 121:1585CrossRefGoogle Scholar
  65. Melin P, Soto J, Castillo O, Soria J (2012) A new approach for time series prediction using ensembles of ANFIS models. Expert Syst Appl 39:3494–3506.  https://doi.org/10.1016/j.eswa.2011.09.040 CrossRefGoogle Scholar
  66. Moreno J (2009) Hydraulic plant generation forecasting in Colombian power market using ANFIS. Energy Econ 31:450–455.  https://doi.org/10.1016/j.eneco.2009.01.012 CrossRefGoogle Scholar
  67. Noori R, Hoshyaripour G, Ashrafi K, Araabi BN (2010) Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmos Environ 44:476–482.  https://doi.org/10.1016/j.atmosenv.2009.11.005 CrossRefGoogle Scholar
  68. Noori R, Safavi S, Nateghi Shahrokni SA (2013) A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand. J Hydrol 495:175–185.  https://doi.org/10.1016/j.jhydrol.2013.04.052 CrossRefGoogle Scholar
  69. 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 39:29–35.  https://doi.org/10.1016/j.ijepes.2012.01.001 CrossRefGoogle Scholar
  70. Prasad K, Gorai AK, Goyal P (2016) Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmos Environ 128:246–262.  https://doi.org/10.1016/j.atmosenv.2016.01.007 CrossRefGoogle Scholar
  71. Pusat S, Akkoyunlu MT, Pekel E et al (2016) Estimation of coal moisture content in convective drying process using ANFIS. Fuel Process Technol 147:12–17.  https://doi.org/10.1016/j.fuproc.2015.12.010 CrossRefGoogle Scholar
  72. Sarıca B, Egrioglu E, Asıkgil B (2016) A new hybrid method for time series forecasting: AR–ANFIS. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2475-5 CrossRefGoogle Scholar
  73. Seo Y, Kim S (2016) River stage forecasting using wavelet packet decomposition and data-driven models. Procedia Eng 154:1225–1230.  https://doi.org/10.1016/j.proeng.2016.07.439 CrossRefGoogle Scholar
  74. Şişman-Yılmaz NA, Alpaslan FN, Jain L (2004) ANFISunfoldedintime for multivariate time series forecasting. Neurocomputing 61:139–168.  https://doi.org/10.1016/j.neucom.2004.03.009 CrossRefGoogle Scholar
  75. Song Q, Chissom BS (1993a) Fuzzy time series and its models. Fuzzy Sets Syst 54:269–277MathSciNetCrossRefGoogle Scholar
  76. Song Q, Chissom BS (1993b) Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst 54:1–9CrossRefGoogle Scholar
  77. Song Q, Chissorn BS (1994) Forecasting enrollments with fuzzy time series-part II. Fuzzy Sets Syst 62:1–8.  https://doi.org/10.1016/0165-0114(94)90067-1 CrossRefGoogle Scholar
  78. Stefanakos C (2016) Fuzzy time series forecasting of nonstationary wind and wave data. Ocean Eng 121:1–12.  https://doi.org/10.1016/j.oceaneng.2016.05.018 CrossRefGoogle Scholar
  79. Sumati V, Chellapilla P, Paul S, Singh L (2016) Parallel interval type-2 subsethood neural fuzzy inference system. Expert Syst Appl 60:156–168.  https://doi.org/10.1016/j.eswa.2016.04.033 CrossRefGoogle Scholar
  80. Sun B, Guo H, Reza Karimi H et al (2015) Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series. Neurocomputing 151:1528–1536CrossRefGoogle Scholar
  81. Sun Y, Tang D, Sun Y, Cui Q (2016) Comparison of a fuzzy control and the data-driven model for flood forecasting. Nat Hazards 82:827–844.  https://doi.org/10.1007/s11069-016-2220-5 CrossRefGoogle Scholar
  82. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC 15:116–132CrossRefGoogle Scholar
  83. Turksen IB (2008) Fuzzy functions with LSE. Appl Soft Comput J 8:1178–1188CrossRefGoogle Scholar
  84. Wang W, Liu X (2015) Fuzzy forecasting based on automatic clustering and axiomatic fuzzy set classification. Inf Sci (Ny) 294:78–94MathSciNetCrossRefGoogle Scholar
  85. Wang W, Li DZ, Vrbanek J (2012) An evolving neuro-fuzzy technique for system state forecasting. Neurocomputing 87:111–119.  https://doi.org/10.1016/j.neucom.2012.02.006 CrossRefGoogle Scholar
  86. Wang L, Liu X, Pedrycz W, Shao Y (2014) Determination of temporal information granules to improve forecasting in fuzzy time series. Expert Syst Appl 41:3134–3142CrossRefGoogle Scholar
  87. Wei L-Y, Cheng C-H, Wu H-H (2014) A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock. Appl Soft Comput 19:86–92.  https://doi.org/10.1016/j.asoc.2014.01.022 CrossRefGoogle Scholar
  88. Xiao Y, Liu JJ, Hu Y et al (2014) A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. J Air Transp Manag 39:1–11.  https://doi.org/10.1016/j.jairtraman.2014.03.004 CrossRefGoogle Scholar
  89. Yang Y, Chen Y, Wang Y et al (2016) Modelling a combined method based on ANFIS and neural network improved by DE algorithm: a case study for short-term electricity demand forecasting. Appl Soft Comput.  https://doi.org/10.1016/j.asoc.2016.07.053 CrossRefGoogle Scholar
  90. Ying L-C, Pan M-C (2008) Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag 49:205–211.  https://doi.org/10.1016/j.enconman.2007.06.015 CrossRefGoogle Scholar
  91. 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 CrossRefGoogle Scholar
  92. 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 MathSciNetCrossRefzbMATHGoogle Scholar
  93. Yu TH-K, Huarng K-H (2008) A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst Appl 34:2945–2952.  https://doi.org/10.1016/j.eswa.2007.05.016 CrossRefGoogle Scholar
  94. Yurdusev MA, Firat M (2009) Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: an application to Izmir, Turkey. J Hydrol 365:225–234.  https://doi.org/10.1016/j.jhydrol.2008.11.036 CrossRefGoogle Scholar
  95. Zadeh La (1965) Fuzzy sets. Inf Control 8:338–353.  https://doi.org/10.1016/S0019-9958(65)90241-X CrossRefzbMATHGoogle Scholar
  96. 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 22:1194–1202.  https://doi.org/10.1016/j.engappai.2009.04.009 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Forecast Research Laboratory, Faculty of EngineeringGiresun UniversityGiresunTurkey
  2. 2.Forecast Research Laboratory, Faculty of Arts and SciencesGiresun UniversityGiresunTurkey
  3. 3.Forecast Research Laboratory, Faculty of Economics and Administrative SciencesGiresun UniversityGiresunTurkey

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