International Journal of Internet of Things and its Applications
| Volume 2, No. 1, 2018, pp 1-6 | ||
| Abstract |
A Study of Seasonal ARIMA Model-Based Forecasting Method for Intelligent Food Control in a Livestock Environment
|
|
Abstract
Most of the high and medium quality hays are imported from different countries for the livestock feedlots. As a fact, increasing production cost is becoming one of the primary problems in the livestock production. To minimize the cost spent on the hay import, the forecasting has to be precise. More than the previous year food stock data; the accumulated feed intake of Beef cattle can give an accurate forecast. Therefore, in this paper, Seasonal - Autoregressive Integrated Moving Average (SARIMA) model is used to forecast the food stock requirement in the livestock barn over a simulated data. The best fit model is identified using the SARIMA model, and the predicted values are compared with the actual data, to provide an accurate forecasting of the food supply.