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  Forecasting Simulation with ARIMA and Combination of Stevenson-Porter-Cheng Fuzzy Time Series  
  Authors : Wahyu Dwi Sugianto; Agus Mohamad Soleh; Farit Mochamad Afendi
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The simulation was implemented to find out the perfomance for a combination of methods in Stevenson-Porter-Cheng Fuzzy Time Series (FTS) based on 100 replicates on 100 generated data following the model of ARIMA (1,0,0) or AR (1). There are 9 scenarios used as a combination between 3 data generation error variance values (0.5, 1, 3) and 3 AR(1) parameter values i.e. 0.3, 0.5, and 0.7. The results of the simulation showed the greater variance of error and the value of the of AR(1) parameter then the variance of the MSE results with ARIMA will be even greater (0.0634 – 15.7633). While the variance of the MSE results forecasting with Cheng and Cheng2 (no sub interval) FTS tend to be more stable (0.0712 – 2.9648 and 0.0640 – 2.7157). By using the percentage change of historical data as the set of universe, SP Cheng FTS produces MSE forecasting range values ranging from 0.0722 – 14.7045. While SP Cheng2 FTS using the difference of historical data resulted in MSE forecasting values ranging from 0.0759 – 4.6803. Although both MSE values do not look much better than Cheng and Cheng2 FTS, but the greater the AR(1) parameter then MSE forecasting of Cheng and Cheng2 FTS will be better than ARIMA and even closer to the Cheng and Cheng2 FTS.


Published In : IJCSN Journal Volume 6, Issue 6

Date of Publication : December 2017

Pages : 806-811

Figures :05

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Wahyu Dwi Sugianto : currently pursuing masters degree program in Applied Statistics in Bogor Agricultural University, Indonesia. Attained bachelor degree from STIS Jakarta in 2006.

Agus Mohamad Soleh : is lecturer at Department of Statistics, Bogor Agricultural University, Indonesia. His main interest is in Data Mining, Statistical Computation and Statistical Modelling.

Farit Mochamad Afendi : is lecturer at Department of Statistics, Bogor Agricultural University, Indonesia. Her main interest is in Geoinformatics.


ARIMA, Cheng Fuzzy Time Series, Simulation, Stevenson-Porter

The results of forecasting with simulation of generated data with ARIMA(1,0,0) or AR(1) with non zero means using 9 scenarios of different (0.5, 1, 3) and 3 parameters ??1 (0.3, 0.5, 0.7) showed that the increased and AR(1) parameter, then the variance of MSE with ARIMA also increased in the range of 0.0634 – 15.7633.


[1] Cheng CH, Chen TL, Teoh HJ, Chiang CH.. Fuzzy Time Series based on Adaptive Expectation Model for TAIEX Forecasting”. Expert System with Application. 34: 1126- 1132. 2008. [2] Dan J, Dong F, Hirota K.. Fuzzy Local Trend Transform based Fuzzy Time Series Forecasting Model. International Journal of Computers, Communication & Control. Vol. VI , No. 4, pp. 603-614. 2011. [3] Hasbiollah M, Hakim RB. F. Peramalan Konsumsi Gas Indonesia Menggunakan Algoritma Fuzzy Time Series Stevenson Porter. Disajikan pada Seminar Nasional Matematika dan Pendidikan Matematika. 2015. Jakarta. Universitas Islam Indonesia. 2015. [4] Montgomery DC, et al. Introduction to Time Series Analysis and Forecasting. Canada: John Wiley and Sons, Inc. 2008. [5] Song Q, Chissom BS. Forecasting enrollments with fuzzy time series-Part I. Fuzzy Sets and Systems. 54:1-9. 1993a. [6] Stevenson M, J. E. Porter. Fuzzy time series forescasting using percentage change as the universe of discourse. Proceedings of World Academy of Science, Engineering and Technology. 55: 154-157. 2009. [7] Tauryawati ML, Irawan MI. Perbandingan Metode Fuzzy Time Series dan Metode Box-Jenkins untuk Memprediksi IHSG. Jurnal Sains dan Seni POMITS Vol. 3, No. 2. 2014. [8] Zhiqiang Z, Qiong Z. Fuzzy time series forescasting based on k-means clustering. Open Journal of Applied Sciences. Supplement: 2012 World Academy of Science, Engineering and Technology. 2012.