Comparison of Methods ARIMA and MAR Models with MODWT Decomposition on Non-Stationary Data

Authors

  • Puce Angreni ISTNUBA
  • Melda Juliza ISTNUBA

DOI:

https://doi.org/10.55123/insologi.v2i2.1888

Keywords:

Nonstasioner, ARIMA, MAR, MODWT

Abstract

The forecasting methods used in this study are Autoregressive Integrated Moving Average (ARIMA) and Multiscale Autoregressive (MAR). The ARIMA model does not include predictor variables in the model. The MAR model is a model that performs the transformation process using wavelets. The MAR model adopts an autoregressive time series (AR) model with wavelet coefficients and scale coefficients as predictors. The wavelet coefficient and scale are obtained by decomposition using Maximal Overlap Discrete Wavelet Transformation (MODWT). MODWT functions to describe data based on the level of each wavelet filter. This study aims to determine the best forecasting model using ARIMA and MAR models. The time series data used in this study is data on the rupiah exchange rate against the US dollar. Data on the rupiah exchange rate against the US Dollar for 2019-2020 is non-stationary data, so the ARIMA and MAR models can be used in this study.

Downloads

Download data is not yet available.

References

Graps, A. 1995. An Introduction to Wavelets. IEEE Computational Science and Engineering. 2 (2).

Renaud, O., Starck, J. L dan Murtagh, F. 2003. Prediction Based on a Multiscale Decomposition. International Journal of Wavelets, Multiresolution and Information Processing. 1(2), 217-232.

Percival, D. B. dan Walden, A. T. (2000). Wavelet Methods for Time Series Analysis. Cambridge University Press, Cambridge.

Montgomery, D. C., Jennings, C. L. dan Kulahci, M. 2008. Introduction Time Series Analysis and Forecasting. John Wiley & Sons, Inc., New Jersey.

Cryer, J. D., dan Chan, K. S. 2008. Time Series Analysis with Application in R. Second Edition. Spinger Science dan Business Media., USA.

Box, G. E. P. dan Jenkins, G. M. 1994. Time Series Analysis: Forecasting and Control. Third Edition. Prentice Hall, New Jersey.

Makridakis, S., Wheelwright, S. C. dan McGee, V. 1999. Metode dan Aplikasi Peramalan. Terjemahan Hari Suminto. Edisi Revisi. Binarupa Aksara, Jakarta.

Popoola, A. O. 2007. Fuzzy-Wavelet Method for Time Series Analysis. Submitted for the degree of doctor of philosophy in Surey University, England.

L. Adi Sopian, Andy Dharmalau, “Perancangan Sistem Informasi Pemesanan Berbasis Web Studi Kasus Pada Restoran Billiechick,” Peranc. Sist. Inf. Pemesanan Berbas. Web Stud. Kasus Pada Restoran Billiechick, vol. 2, no. 5, p. 4, 2020.

A. F. Qadafi and A. D. Wahyudi, “Sistem Informasi Inventory Gudang Dalam Ketersediaan Stok Barang Menggunakan Metode Buffer Stok,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 1, no. 2, pp. 174–182, 2020, doi: 10.33365/jatika.v1i2.557.

Downloads

Published

2023-04-29

How to Cite

Angreni, P., & Melda Juliza. (2023). Comparison of Methods ARIMA and MAR Models with MODWT Decomposition on Non-Stationary Data. INSOLOGI: Jurnal Sains Dan Teknologi, 2(2), 392–399. https://doi.org/10.55123/insologi.v2i2.1888

Issue

Vol. 2 No. 2 (2023): April 2023

Section

Articles

License

Copyright (c) 2023 Puce Angreni, Melda Juliza

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Hak cipta pada setiap artikel adalah milik penulis.
Penulis mengakui bahwa INSOLOGI (Jurnal Sains dan Teknologi) sebagai publisher yang mempublikasikan pertama kali dengan lisensi Creative Commons Attribution 4.0 International License.
Penulis dapat memasukan tulisan secara terpisah, mengatur distribusi non-ekskulif dari naskah yang telah terbit di jurnal ini kedalam versi yang lain, seperti: dikirim ke respository institusi penulis, publikasi kedalam buku, dan lain-lain. Dengan mengakui bahwa naskah telah terbit pertama kali pada INSOLOGI (Jurnal Sains dan Teknologi).