Incorporating Decomposition and the Holt-Winters Method into the Whale Optimization Algorithm for Forecasting Monthly Government Revenue in Thailand
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Abstract
This study focuses on the forecasting of government revenue in Thailand across four primary sectors: the Revenue Department, Excise Department, Customs Department, and Other Agencies. Acknowledging the critical role of precise and efficient forecasting in policymaking, we proposed two models: the Whale Optimization Algorithm with Holt-Winters (WOA-HW) and the Whale Optimization Algorithm with Decomposition (WOA-D), comparing their performance with two classical models: Classical Decomposition (Classic-D) and Box-Jenkins. The model performances were evaluated using both a training dataset and a test dataset, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) serving as key metrics. The results demonstrate that the WOA-D model generally outperformed the other models during the training phase, showcasing its significant potential in time series forecasting. During the testing phase, the WOA-HW model exhibited commendable performance across three datasets: the Revenue Department, Excise Department, and Other Agencies. For the Customs Department dataset, the Box-Jenkins model emerged as the top performer, employing a 12 model. This study concludes by emphasizing the effectiveness of these models not only for forecasting government revenue but also for broader applicability in forecasting other time series data.
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