Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting
Saxena-Easo Fuzzy Time Series (FTS) is a softcomputing method for forecasting using fuzzy concept. It doesn’t need any assumption like conventional forecasting method. Generally it’s focused on three important steps like percentage change as the universe of discourse, interval partition, and defuzzification. In this research, this method is applied to Indonesia’s inflation rate data. The aim of this research is to forecast Indonesia’s inflation rate in 2017 by using input from Autoregressive Integrated Moving Average (ARIMA) process, Saxena-Easo FTS, and actual data from 1970-2016. ARIMA is focused on four steps like identifying, parameter estimation, diagnostic checking, and forecasting. The result for Indonesia’s inflation rate forecasting in 2017 is about 5.9182 using Saxena-Easo FTS. Root Mean Square Error (RMSE) is also computed to compare the accuracy rate from each method between Saxena-Easo FTS and ARIMA. RMSE from Saxena-Easo FTS is about 0.9743 while ARIMA is about 6.3046.
Keywords: saxena-easo fuzzy time series, ARIMA, inflation rate, RMSE.
Box, G.E.P. & Jenkins, G.M. 1976. Time Series Analysis, Forecasting and Control. San Francisco: Holden-Day.
Jilani, T.A, Burney, S.M.A. & Ardil, C. 2007. Fuzzy Metric Approach for Fuzzy Time Series Forecasting based on Frequency Density Based Partitioning. Proceedings of World Academy of Science, Engineering and Technology. 34 : 333-338.
Kesumawati, A. & Primandari, A.H. 2015. Forecasting BI Rate Based on Fuzzy Time Series with Higher Forecast Accuracy Rate.Proceedings International Conference on Mathematics, Sciences and Education, University of Mataram 2015, Lombok Island, Indonesia,November 4-5. MATH 44- MATH 48.
Saxena, P. & Easo, S. 2012. A New Method for Forecasting Enrollments based on Fuzzy Time Series with Higher Forecast Accuracy Rate. International Journal of Computer Technology & Applications. 3 : 2033-2037.
Song, Q. & Chissom, B.S. 1993. Fuzzy time series and its models. Fuzzy Sets and Systems. 54 : 269-277.
Stevenson, M. & Porter, J.E. 2009. Fuzzy Time Series Forecasting Using Percentage Change as The Universe of Discourse. Proceedings of World Academy of Science, Engineering and Technology. 55: 154-157.
Supardi, U.S. 2016. Aplikasi Statistika Dalam Penelitian Edisi Revisi. Jakarta Selatan: Change Publication.
Suseno & Astiyah, S. 2009. Inflasi. Jakarta: Pusat Pendidikan dan Studi Kebanksentralan (PPSK) BI.
Taylor, J. W. 2003. Short-Term Electricity Demand Forecasting Using Double Seasonal Exponential Smoothing. Journal of Operational Research Society. 54: 799-805.
Wei, W.W.S. 2006. Time Series Analysis. California:Addison-Wesley Publishing Company Inc.
Wooldridge, J.M. 2013. Introductory Econometrics A Modern Approach 5th Edition. USA : South – Western College Pub.
Zadeh, L.A. 1965. Fuzzy Sets. Information and Control. 8: 338-353.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Author who publish with Jurnal ILMU DASAR journal agree to share and copy the work with acknowledgment of the work's authorship and initial publication in this journal.