MODEL HIBRIDA DEKOMPOSISI-ARIMA UNTUK PERAMALAN INFLASI DI KOTA MAKASSAR
(1) Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar, Indonesia
(2) Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar, Indonesia
(*) Corresponding Author
DOI: https://doi.org/10.35580/variansiunm23889
Abstract
Forecasting is an art and predicting science about future events. Forecasting could be basic for short-term, mid-term, and long-term planning. The aim of this study is to create a hybrid decomposition model - ARIMA to forecast inflation data in Makassar City. The decomposition method is used for decomposition the inflation data into trend components, seasonal, and random. Furthermore, the decomposition method could be used to forecasting the tren component dan seasonal. Whereas, the ARIMA method was used to forecasting the random component. The result of this study shows ARIMA model used for forecasting the random component is ARIMA (0,0,[3]) with an AIC score of 171,6973
Keywords: Decomposition, ARIMA, inflation
Full Text:
PDFReferences
Badan Pusat Statistik. (2018). Inflasi 2019 [Online]. Dikutip pada 7 Oktober 2020, dari https://sulsel.bps.go.id/indicator/3/1/1/inflasi.html.
Heizer, J. Render, B. (2011). Operations Management. Global Edition. Tenth Edition. Pearson.
Luo, H. Wang, D. Yue, C. Liu, Y. Guo, H. (2017). Research and application of a
novel hybrid decomposition-ensemble learning paradigm with error
correction for daily PM10 forecasting. Atmospheric Research, 201, 34-45.
Makridakis, S. Wheelwright, S.C. Hyndman, R.J. (1997). Forecasting Methods and Applications. Wiley.
Septia Tika. (2012). Penggunaan metode dekomposisi-ARIMA dalam meramalkan tingkat pengembalian saham pada emitmen terpilih di bursa efek Indonesia periode 2003-2007
Article Metrics
Abstract view : 283 times | PDF view : 140 timesRefbacks
- There are currently no refbacks.
Copyright (c) 2021 Muhammad Fahmuddin, Zulkifli Rais
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Abstracted/Indexed by:
VARIANSI: Journal of Statistics and Its Application on Teaching and Research is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)