Comparison of Methods ARIMA and MAR Models with MODWT Decomposition on Non-Stationary Data - Angreni, Puce; Melda Juliza (Yayasan Literasi Sains Indonesia, 2023)
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.