Research Article
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A computational method based on interval length for fuzzy time series forecasting

Year 2021, , 22 - 33, 01.06.2021
https://doi.org/10.46572/naturengs.882203

Abstract

n the literature, there have been a good many different forecasting methods related forecasting problems of fuzzy time series. The main issue of fuzzy time series forecasting is the accuracy of the forecasted values. The forecasting accuracy rate is affected by the length of each interval in the universe of discourse. Thus, it is substantial to determine the length of each interval. In this study, new computational method based on class width to determine interval length is proposed and also used coefficient of variation for time series forecasting. After the intervals are formed, the historical time series data set is fuzzified according to fuzzy time series theory. The proposed model has been tested on the student enrollments, University of Alabama and a real life problem of rice production for containing higher uncertainty. This method was compared with existent methods to determine the effectiveness in terms of the mean square error (MSE) and the average forecasting (AFE). The results are shown that the proposed model can achieve a higher forecasting accuracy rate than the existent models.

References

  • [1] Hwang, J.R., Chen, S.M. and Lee, C.H., 1998. Handling forecasting problems using fuzzy time series. Fuzzy sets and systems, 100(1-3), 217-228 . [2] Gangwar, S.S., Kumar, S., 2012. Partitions based computational method for high-order fuzzy time series forecasting. Expert Systems with Applications, 39(15), 12158-12164.
  • [3] Singh, S.R., 2008. A computational method of forecasting based on fuzzy time series. Mathematics and Computers in Simulation, 79(3), 539-554.
  • [4] Zadeh, L. A. 1965. Fuzzy sets. Information and control, 8(3), 338-353.
  • [5] Song, Q., Chissom, B.S., 1993. Forecasting enrollments with fuzzy time series—part I. Fuzzy sets and systems, 54(1), 1-9.
  • [6] Huarng, K., 2001. Heuristic models of fuzzy time series for forecasting. Fuzzy sets and systems, 123(3), 369-386.
  • [7] Singh, S.R., 2007. A robust method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 188(1), 472-484.
  • [8] Singh, S.R., 2007. A simple method of forecasting based on fuzzy time series. Applied mathematics and computation, 186(1), 330-339.
  • [9] Singh, S.R., 2007. A simple time variant method for fuzzy time series forecasting. Cybernetics and Systems: An International Journal, 38(3), 305-321.
  • [10] Singh, P., Borah, B., 2013. An efficient time series forecasting model based on fuzzy time series. Engineering Applications of Artificial Intelligence, 26(10), 2443-2457.
  • [11] Cheng, C. H., Wang, J. W., & Cheng, G. W. (2008). Multi-attribute fuzzy time series method based on fuzzy clustering. Expert Systems with Applications, 34, 1235–1242.
  • [12] Wong, W.K., Bai, E. and Chu, A.W.C., 2010. Adaptive time-variant models for fuzzy-time-series forecasting. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(6), 1531-1542.
  • [13] Chen, S.M., Tanuwijaya, K., 2011. Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Systems with Applications, 38(12), 15425-15437.
  • [14] Chen, S.M., 1996. Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems, 81(3), 311-319.
Year 2021, , 22 - 33, 01.06.2021
https://doi.org/10.46572/naturengs.882203

Abstract

References

  • [1] Hwang, J.R., Chen, S.M. and Lee, C.H., 1998. Handling forecasting problems using fuzzy time series. Fuzzy sets and systems, 100(1-3), 217-228 . [2] Gangwar, S.S., Kumar, S., 2012. Partitions based computational method for high-order fuzzy time series forecasting. Expert Systems with Applications, 39(15), 12158-12164.
  • [3] Singh, S.R., 2008. A computational method of forecasting based on fuzzy time series. Mathematics and Computers in Simulation, 79(3), 539-554.
  • [4] Zadeh, L. A. 1965. Fuzzy sets. Information and control, 8(3), 338-353.
  • [5] Song, Q., Chissom, B.S., 1993. Forecasting enrollments with fuzzy time series—part I. Fuzzy sets and systems, 54(1), 1-9.
  • [6] Huarng, K., 2001. Heuristic models of fuzzy time series for forecasting. Fuzzy sets and systems, 123(3), 369-386.
  • [7] Singh, S.R., 2007. A robust method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 188(1), 472-484.
  • [8] Singh, S.R., 2007. A simple method of forecasting based on fuzzy time series. Applied mathematics and computation, 186(1), 330-339.
  • [9] Singh, S.R., 2007. A simple time variant method for fuzzy time series forecasting. Cybernetics and Systems: An International Journal, 38(3), 305-321.
  • [10] Singh, P., Borah, B., 2013. An efficient time series forecasting model based on fuzzy time series. Engineering Applications of Artificial Intelligence, 26(10), 2443-2457.
  • [11] Cheng, C. H., Wang, J. W., & Cheng, G. W. (2008). Multi-attribute fuzzy time series method based on fuzzy clustering. Expert Systems with Applications, 34, 1235–1242.
  • [12] Wong, W.K., Bai, E. and Chu, A.W.C., 2010. Adaptive time-variant models for fuzzy-time-series forecasting. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 40(6), 1531-1542.
  • [13] Chen, S.M., Tanuwijaya, K., 2011. Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Systems with Applications, 38(12), 15425-15437.
  • [14] Chen, S.M., 1996. Forecasting enrollments based on fuzzy time series. Fuzzy sets and systems, 81(3), 311-319.
There are 13 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Özlem Akay

Publication Date June 1, 2021
Submission Date February 17, 2021
Acceptance Date March 26, 2021
Published in Issue Year 2021

Cite

APA Akay, Ö. (2021). A computational method based on interval length for fuzzy time series forecasting. NATURENGS, 2(1), 22-33. https://doi.org/10.46572/naturengs.882203