Fuzzy Time Series Forecasting Accuracy Based on Hybrid Similarity Measure
Keywords:Hybrid Similarity Measure, Fuzzy Time Series Forecasting, Forecasting Accuracy
The majority of fuzzy time series forecasting (FTSF) algorithms assess forecasting accuracy using an error-based distance. The predicted value is defuzzified to a crisp number and the error-based distance will be computed. Defuzzification causes some information to be lost, which leads to its inability to comprehend the level of uncertainty that has been preserved during the forecasting process. This paper proposes an enhanced FTSF model with forecasting accuracy developed based on a new hybrid similarity measure combining the centre of gravity and area and height. Three properties of the hybrid similarity measure are presented. The FTSF model is implemented in the case of the Malaysian unemployment rate. The findings indicate that, on average more than 94% of the predicted value was identical to historical data. The forecasting accuracy is produced directly from the forecasting value without undergoing the defuzzification process, which can preserve some information from being lost.
Alam NMFHNA, Ramli N, Mohammed N. (2021). Fuzzy time series forecasting model based on intuitionistic fuzzy sets via delegation of hesitancy degree to the major grade de-i-fuzzification method. Mathematics and Statistics, 9(1), 46-53.
Bisht K, Kumar S. (2016). Fuzzy time series forecasting method based on hesitant fuzzy sets. Expert Systems with Applications, 64, 557-568.
Chen SM. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets Systems, 81, 311-319.
Chen MY. (2014). A high-order fuzzy time series forecasting model for internet stock trading. Future Generation Computer Systems, 37, 461-467.
Chen SJ, Chen, SM. (2001). A new method to measure the similarity between fuzzy numbers. Proceedings of the 10th IEEE International Conference on Fuzzy Systems, p. 208-214.
Chen SM, Phuong BDH. (2017). Fuzzy time series forecasting based on optimal partition of intervals and optimal weighting vectors. Knowledge-Based Systems, 118, 204-216.
Cheng CH, Chen CH. (2018). Fuzzy time series model based on weighted association rule for financial market forecasting. Expert Systems, 35(4), e12271.
Department of Statistic Malaysia. Time series data of unemployment. Accessed January 13, 2014.
Gamayanti NH, Junaida J, Fitri F. (2023). Application of fuzzy time series to forecast COVID-19 cases in Central Sulawesi. AIP Conference Proceedings, 2719, 040001.
Gupta KK, Kumar S. (2019). Fuzzy time series forecasting method using probabilistic fuzzy sets. In Mandal, J., Bhattacharyya, D., Auluck, N. (Eds.). Advanced Computing and Communication Technologies, Springer, Singapore.
Hanif R, Mustafa S, Iqbal S, Piracha S. (2023). A study of time series forecasting enrollments using fuzzy interval partitioning method. Journal of Computational and Cognitive Engineering, 2(2), 143-149.
Hejazi SR, Doostparast A, Hosseini SM. (2011). An improved fuzzy risk analysis based on new similarity measure of generalized fuzzy numbers. Expert Systems with Applications, 38, 9179-9185.
Hsieh CH, Chen SH. (1999). Similarity of generalized fuzzy numbers with graded mean integration. Proceedings of the 8th International Fuzzy System Association World Congress, 2, p. 551-555.
Khatoon S, Ibraheem, Gupta P, Shahid M. (2023). Comparison of fuzzy time series, ANN, and wavelet techniques for short term load forecasting. International Journal of Power Electronics and Drive Systems, 14(2), 1260-1269.
Kuo IH, Horng SJ, Kao TW, Lin TL, Lee CL, Pan Y. (2009). An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Systems with Applications, 36(3), 6108-6117.
Liu H. (2007). An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers. Fuzzy Optimization and Decision Making, 6, 63-80.
Liu HT. (2009). An integrated fuzzy time series forecasting system. Expert Systems with Applications, 36(6), 10045-10053.
Pal SS, Kar S. (2019). Fuzzy time series model for unequal interval length using genetic algorithm. Advances in Intelligent Systems and Computing, 699, 205-216.
Patra K, Mondal SK. (2015). Fuzzy risk analysis using area and height based similarity measure on generalized trapezoidal fuzzy numbers and its application. Applied Soft Computing, 28, 276-284.
Ramli N, Tap AOM. (2009). Forecasting students’ enrolment in fuzzy time series based on three classes of t-norm of subsethood defuzzification. Gading Business and Management Journal, 13(1), 1-14.
Singh SR. (2007). A simple method of forecasting based on fuzzy time series. Applied Mathematics and Computation, 186, 330-339.
Solikhin, Lutfi S, Purnomo, Hardiwinoto. A machine learning approach in Phyton is used to forecast the number of train passengers using a fuzzy time series model. Bulletin of Electrical Engineering and Informatics, 11(5), 2746-2755.
Song Q, Chissom BS. (1993). Forecasting enrollments with fuzzy time series-Part I. Fuzzy Sets and Systems, 54, 1-9.
Song Q, Chissom BS. (1994). Forecasting enrollments with fuzzy time series-Part II. Fuzzy Sets and Systems, 62, 1-8.
Tinh NV, Dieu NC. (2017). A new hybrid fuzzy time series forecasting model combined the time variant fuzzy logical relationship groups with particle swam optimization. Computer Science and Engineering, 7(2), 52-66.
Wang LX (1997). A course in fuzzy systems and control. Prentice-Hall.
Xu Z, Shang S, Qian W, Shu W. (2010). A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers. Expert Systems with Applications, 37(3), 1920-1927.
How to Cite
Copyright (c) 2023 Nazirah Ramli, Siti Musleha Ab Mutalib, Daud Mohamad, Mahmod Othman, Asyura Abd Nassir
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.