PERBANDINGAN METODE EXPONENTIAL SMOOTHING DAN ARIMA PADA PERAMALAN GARIS KEMISKINAN PROVINSI JAWA TENGAH - Afifah Zahrunnisa; Renanta Dzakiya Nafalana; Istina Alya Rosyada; Edy Widodo (LPPM Universitas Bina Bangsa, 2021)
Forecasting is a technique that uses past data or historical data to determine something in the future. Forecasting methods with time series models consist of several methods, such as Double Exponential Smoothing (Holt method) and ARIMA. DES (Holt method) is a method that is used to predict time series data that has a trend pattern. ARIMA model combines AR and MA models with differencing order d. The poverty line is calculated by finding the total cost of all the essential resources that an average human adult consumes in one year. The lack of poverty reduction in an area is the lack of information about poverty. The selection of the forecasting method was made by considering several things. The Exponential Smoothing method was chosen because this method was able to predict time series financial data well and revise prediction errors. While the ARIMA method is better for short-term prediction, it can predict values that are difficult to explain by economic theory and are efficient in predicting time series financial data. There is still little research on comparing time series data for forecasting methods. Researchers are interested in comparing the Exponential Smoothing and ARIMA methods in implementing poverty line forecasting in Central Java. The two methods are compared by determining the best method for forecasting the poverty line in Central Java. The best forecasting method can be seen from the MAPE value of each method