The MODWT-ARIMA Model in Forecasting  The COVID-19 Cases

Nurfitri Imro'ah; Nur'ainul Miftahul Huda

doi:10.31764/jtam.v7i4.16465

The MODWT-ARIMA Model in Forecasting The COVID-19 Cases

Nurfitri Imro'ah, Nur'ainul Miftahul Huda

Abstract

The Maximal Overlap Discrete Wavelet Transform-Autoregressive Integrated Moving Average (MODWT-ARIMA) is a forecasting method that uses the ARIMA model generated from MODWT data. The purpose of this study is to analyze an investigation into the MODWT-ARIMA model with regard to the total number of COVID-19 cases in DKI Jakarta. For this study, daily data on cases of Covid-19COVID-19 in DKI Jakarta were obtained. The model is trained with data from April 3, 2022, to June 11, 2022 (referred to as the "in-sample"), and the outcomes of the prediction are tested with data from June 12, 2022, to June 18, 2022 (referred to as the "out-sample"). These data exhibit trends and are organizedorganised into four data series using MODWT. The ARIMA modelling technique is applied to each of the produced sequences. When using the MODWT-ARIMA approach, the RMSE value obtained from the in-sample data is found to be lower than the RMSE value obtained from the out-sample data. In light of the findings, Iit t became clear that MODWT-ARIMA is better suited for estimation than prediction. The fact that the RMSE value for the data acquired from the in-sample is lower than the RMSE value for the data collected from the out-sample demonstrates that this is the case.

Keywords

Daubechies4; Scale coefficient; Wavelet coefficient.

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DOI: https://doi.org/10.31764/jtam.v7i4.16465