Over-dues forecasting using ARIMA Technique

The work presented in this paper establishes an enrichment in modeling and forecasting over-dues for Beverages manufacturing company. A time-series modeling technique used to forecast over-dues for ABinBEV (Beer manufacturing company). Our work demonstrates how historical over-dues data utilized to predict future over-dues. The historical over-dues information used to develop several Autoregressive Integrated Moving Average (ARIMA) models by using Root mean squared error (RMSE) and the most suitable ARIMA model found to be ARIMA (2, 1, 0). and validation performed by comparing the accuracy of the models with three types of accuracy criteria, which are Mean square error (MSE), Root Mean Squared Error (RMSE), and Mean absolute error (MAE).