EVALUATING THE ACCURACY OF HOLT'S DOUBLE EXPONENTIAL SMOOTHING AND ARIMA IN FORECASTING BATANG REGENCY'S GROSS REGIONAL DOMESTIC PRODUCT (2025–2029) - Alviyatur Rahmaniyah; Glagah Eskacakra Setyowisnu (Parameter: Journal of Statistics, 2026)
Batang Regency has experienced continuous economic growth in recent years, making information on changes in Gross Regional Domestic Product (GRDP) essential for supporting regional development planning. Reliable forecasting methods are needed to provide an overview of future economic conditions based on observed trends. Previous studies have applied either Holt’s Double Exponential Smoothing (DES) or Autoregressive Integrated Moving Average (ARIMA) models for economic forecasting; however, comparative evidence regarding the performance of these methods on Batang Regency’s GRDP data remains limited. Therefore, this study aims to compare the forecasting performance of both methods and generate GRDP forecasts for the period 2025–2029. This study utilized annual GRDP data at constant prices from 2010 to 2024, which showed an increasing trend and nonstationary characteristics. The analysis procedure included descriptive analysis, stationarity testing using the Augmented Dickey–Fuller (ADF) test, model estimation, and forecast accuracy evaluation using Mean Absolute Percentage Error (MAPE). The ADF test indicated that the series became stationary after first-order differencing, and several ARIMA models were evaluated using the Akaike Information Criterion (AIC), resulting in ARIMA(1,1,1) being selected as the best-performing model. However, in terms of forecasting accuracy, Holt’s DES outperformed ARIMA by producing a lower MAPE value of 2.15%, compared to 8.87% for ARIMA(1,1,1). Forecasts generated by both methods indicate that Batang Regency’s GRDP is expected to continue increasing during the period 2025–2029. These findings demonstrate that Holt’s DES provides more accurate forecasts for the GRDP data used in this study and contribute empirical evidence regarding the comparative performance of classical time series forecasting methods for regional economic planning. The results may serve as a reference for policymakers in formulating more targeted and sustainable development strategies.