Model Time Series untuk Meramalkan Tingkat Kemiskinan di Aceh - Safwandi; Zefri Maulana (Jurnal Investasi Islam, 2024)
Poverty tends to occur in developing countries, including Indonesia. Poverty is a complex problem and requires a comprehensive approach in efforts to eradicate it. Handling poverty must be carried out appropriately and cover various aspects of people's lives. The negative impacts of poverty not only affect people's welfare, but also hinder economic development in the long term. Poverty has a negative effect on the economy and social welfare. To determine the prediction of poverty levels, one approach that can be taken is a time series model (time series). In this research, method Autoregressive Integrated Moving Average (ARIMA) is used for analysis and forecasting poverty levels. So that it can provide insight into poverty behavior and take appropriate steps both in controlling and alleviating poverty. ARIMA is a time series forecasting method that is suitable for forecasting various variables quickly, simply, cheaply and accurately. The research results show that the poverty rate for the next three years (2024-2026) is 14.17%, 13.93% and 13.72%, respectively. The ARIMA model is proven to be able to provide fairly accurate predictions and follow actual trends, making it useful for forecasting. The ARIMA(1,1,1) model can be used as a reference for future forecasting. The process of forecasting poverty levels does not only focus on predicting statistical figures, but also includes an in-depth analysis of the factors that cause poverty and the economic dynamics that influence it. This allows the government to develop policies that are more effective and responsive to social and economic changes occurring in society.