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 defuzzi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Fakultas MIPA Universitas Jember
2019-01-01
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| Series: | Jurnal Ilmu Dasar |
| Online Access: | https://jurnal.unej.ac.id/index.php/JID/article/view/6881 |
| _version_ | 1818859047570374656 |
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| author | Lutvia Citra Ramadhani Dian Anggraeni Ahmad Kamsyakawuni |
| author_facet | Lutvia Citra Ramadhani Dian Anggraeni Ahmad Kamsyakawuni |
| author_sort | Lutvia Citra Ramadhani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T09:05:58Z |
| format | Article |
| id | doaj.art-267aa58ebfe14a99acdb82d31a667476 |
| institution | Directory Open Access Journal |
| issn | 1411-5735 2442-5613 |
| language | English |
| last_indexed | 2024-12-19T09:05:58Z |
| publishDate | 2019-01-01 |
| publisher | Fakultas MIPA Universitas Jember |
| record_format | Article |
| series | Jurnal Ilmu Dasar |
| spelling | doaj.art-267aa58ebfe14a99acdb82d31a6674762022-12-21T20:28:20ZengFakultas MIPA Universitas JemberJurnal Ilmu Dasar1411-57352442-56132019-01-01201536010.19184/jid.v20i1.68816881Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate ForecastingLutvia Citra Ramadhani0Dian Anggraeni1Ahmad Kamsyakawuni2Jurusan Matematika, FMIPA Universitas Jember, JemberJurusan Matematika, FMIPA Universitas Jember, JemberJurusan Matematika, FMIPA Universitas Jember, JemberSaxena-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.https://jurnal.unej.ac.id/index.php/JID/article/view/6881 |
| spellingShingle | Lutvia Citra Ramadhani Dian Anggraeni Ahmad Kamsyakawuni Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting Jurnal Ilmu Dasar |
| title | Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting |
| title_full | Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting |
| title_fullStr | Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting |
| title_full_unstemmed | Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting |
| title_short | Saxena-Easo Fuzzy Time Series on Indonesia’s Inflation Rate Forecasting |
| title_sort | saxena easo fuzzy time series on indonesia s inflation rate forecasting |
| url | https://jurnal.unej.ac.id/index.php/JID/article/view/6881 |
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