A Comparative Analysis of Exponential Smoothing Methods for Forecasting Poverty Data
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Abstract
One of the primary challenges in enhancing a nation's welfare is the prevalence of poverty. This issue is also evident in Indonesia. According to data from the Central Statistics Agency (BPS) of Central Lampung, the poverty rate in Central Lampung Regency has shown a declining trend from 2005 to 2024. In this context, poverty is defined as the population whose average monthly expenditure falls below the regional poverty line. To anticipate potential increases in the number of people living in poverty, forecasting methods such as single exponential smoothing, double exponential smoothing, and triple exponential smoothing can be employed to estimate future poverty rates. Based on a comparative analysis of these three methods, the results indicate that the triple exponential smoothing method provides the highest predictive accuracy, with a Mean Absolute Percentage Error (MAPE) of 5.431, a Mean Absolute Deviation (MAD) of 10.502, and a Mean Squared Deviation (MSD) of 256.596. The projected poverty rate for the year 2025 using this method is 158.931.
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