Search Result For 'Applied Mathematics 2019, 9(1): 1-5 DOI: 10.5923/j.am.20190901.01 Role of Time Series Analysis in Forecasting Egg Production Depending on ARIMA Model Fatma D. M. Abdallah Department of Animal Wealth Development, Faculty of Veterinary Medicine, Zagazig University, Egypt Abstract The goal of this study is to show the role of time series models in predicting process and to demonstrate the suitable type of it according to the data under study. Autoregressive integrated moving averages (ARIMA) model is used as a common and a more applicable model. Univariate ARIMA model is used here to forecast egg production in some layers depending on daily data from the period of May to October 2018. Different criteria of the ARIMA model can be used to choose the suitable one such as the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute relative percentage error (MARPE). Depending on these measures the autoregressive integrated moving average model with ordering (2,2,1) is considered the best model for forecasting process. The model fit statistics such as RMSE (331.520) which was low and the lowest BIC value (11.745) indicating that the model fit the data well. The high value of R2 (0.95) and MAPE (4.542) indicated a perfect forecasting model. Also, ARIMA model with ordering (1,2,2) is good in prediction process.' , Result Number : 1