Comparison between individual and hybrid approaches for estimating fuzzy time series

Publish Year: 1394
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

ICMI02_187

تاریخ نمایه سازی: 26 شهریور 1395

Abstract:

Forecasting is one of the methods that helps decision makers to decide better about future. One of the best tools for forecasting is statistical methods. There are several criteria to measure the performance of forecasting methods that one of the most important of them is analysis the forecast error variance. Forecast combination methods are proposed to improve the accuracy of the forecasting. On the other hand, in practical applications, usage of fuzzy logic because of the vagueness and uncertainty is necessary. In the last decade, forecasting based on fuzzy time series has been used but the combination of fuzzy time series techniques to improving forecast accuracy has not been utilized. In current paper, to forecast linear process, fuzzy regression and other forecasting methods such as fuzzy double exponential smoothing are applied. After that combination of these three methods the forecast is utilized. The results imply that the forecast error variance in hybrid method is improved. These results are proved for a numerical example.

Keywords:

Fuzzy time series , Combination of forecasting methods , Fuzzy regression

Authors

S.M.T Fatemi ghomi

Full professor, Faculty of Industrial Engineering & Management Systems, Amirkabir University of Technology-Tehran Polytechnic, Tehran, Iran

S Jaberi

Faculty of Industrial Engineering & Management Systems, Amirkabir University of Technology-Tehran Polytechnic, Tehran, Iran

M Hajian-Heidary

Faculty of Industrial Engineering & Management Systems, Amirkabir University of Technology-Tehran olytechnic, Tehran, Iran

Moeen Sammak Jalali

PhD Candidate, Faculty of Industrial Engineering & Management Systems, Amirkabir University of Technology-Tehran Polytechnic, Tehran, Iran

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