Abstract
One of the efficient ways for obtaining accurate forecasts is usage of forecast combination method. This approach consists of combining different forecast values obtained from different forecasting models. Also artificial neural networks and fuzzy time series approaches have proved their success in the field of forecasting. In this study, a new forecast combination approach based on artificial neural networks is proposed. The forecasts obtain from different fuzzy time series models are combined by utilizing artificial neural networks. The proposed method is applied to index of Istanbul stock exchange (IMKB) time series and the results are compared to other forecast combination methods available in the literature. As a result of the implementation, it is seen that the proposed forecast combination approach produces better forecasts than those produced by other methods.
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Aladag CH, Basaran MA, Egrioglu E, Yolcu U, Uslu VR (2009) Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations. Expert Syst Appl 36: 4228–4231
Bates JM, Granger CWJ (1969) The combination of forecast. Oper Res Q 20(4): 451–468
Chen SM (1996) Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst 81: 311–319
Chen SM (2002) Forecasting enrollments based on high order fuzzy time series. Cybern Syst 33: 1–16
Cheng CH, Cheng GW, Wang JW (2008) Multi-attribute fuzzy time series method based on fuzzy clustering. Expert Syst Appl 34: 1235–1242
Egrioglu E, Aladag CH, Günay S (2008) A new model selection strategy in artificial neural network. Appl Math Comput 195: 591–597
Egrioglu E, Aladag CH, Uslu VR, Basaran MA, Yolcu U (2009) A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model. Expert Syst Appl 36(4): 7424–7434
Freitas PSA, Rodrigues AJL (2006) Model combination in neural-based forecasting. Eur J Oper Res 173: 801–814
Granger CWJ, Ramanathan R (1984) Improved methods of combined forecasts. J Forecast 3: 197–204
Huarng K (2001) Effective length of intervals to improve forecasting in fuzzy time-series. Fuzzy Sets Syst 123: 387–394
Levenberg K (1944) A method for the solution of certain non-linear problems in least squares. Q Appl Math 2: 164–168
Newbold P, Granger CWJ (1974) Experience with forecasting time series and combination of forecasts. J R Stat Soc A 137(2): 131–165
Song Q, Chissom BS (1993) Fuzzy time series and its models. Fuzzy Sets Syst 54: 269–277
Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst 54: 1–10
Winkler RL, Markidakis S (1983) The combination of forecasts. J R Stat Soc A 146(2): 150–157
Wong KKF, Song H, Witt SF, Wu DC (2007) Tourism forecasting: to combine or not to combine?. Tour Manag 28: 1068–1078
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Aladag, C.H., Egrioglu, E. & Yolcu, U. Forecast Combination by Using Artificial Neural Networks. Neural Process Lett 32, 269–276 (2010). https://doi.org/10.1007/s11063-010-9156-7
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DOI: https://doi.org/10.1007/s11063-010-9156-7