Using of Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Milk Production of Dairy Cattle Farms in Dakahlia Governarate of Egypt
Using of Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Milk Production of Dairy Cattle Farms in Dakahlia Governarate of Egypt
Asmaa A. Badr, Eman A. Abo Elfadl, Mohammed M. Fouda, Sayed M. Elsayed
ABSTRACT
This study was carried out on yearly time series data from 2013-2021 aimed to forecast milk production in two different farms of Holstein Friesian and Holstein German in Dakahlia governorate of Egypt using Autoregressive Integrated Moving Average (ARIMA) model. Data of daily milk production (kg) of two farms were collected to get total milk production (kg) through 305 days during period of 2013-2021 during COVID-19 occurrence. The study employed stationary of data by checking out Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). After confirming stationarity, Akaike information Criteria (AIC), Schwartz Bayesian Information Criteria (SBIC), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used to test the reliability of the model. Autoregressive Integrated Moving Average (ARIMA) model was used to conduct the results. Our study forecasted milk production by using ARIMA model from 2022 to 2033. ARIMA forecasting results showed that milk production will be increased in 2022 and 2023 for Holstein Friesian farm. Meanwhile, milk production will be increased in 2022 and will be steadily increased for the following years in Holstein German farm. The results also indicated that ARIMA (2,1,2) is the best fit model for Holstein Friesian in the first farm. Meanwhile, the ARIMA (0,1,2) is the best model for Holstein German in the second farm.
Keywords | ARIMA, Milk production, Time series, AIC, Holstein Friesian
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July 2022
Vol. 10, Iss. 7, Pages 1423-1658
