Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting

Lutvia Citra Ramadhani; Dian Anggraeni; Ahmad Kamsyakawuni

doi:10.19184/jid.v20i1.6881

Lutvia Citra Ramadhani Jurusan Matematika, FMIPA Universitas Jember, Jember
Dian Anggraeni Jurusan Matematika, FMIPA Universitas Jember, Jember
Ahmad Kamsyakawuni Jurusan Matematika, FMIPA Universitas Jember, Jember

DOI: https://doi.org/10.19184/jid.v20i1.6881

Abstract

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.

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