Forecasting with ARIMA

In ARIMA modeling, the order of the autoregressive component is frequently zero, one or sometimes two. Therefore, only short forecasting intervals are of any use. Disturbances in future values, normally smoothed by the moving average, were set to zero. The following example demonstrates this fact  

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To develop the tool, we have considered only simple forecasting methods, such as moving averages and exponential smoothing, so that each level of the chain uses the best one that suits the demand it should deal with. With them, it is possible to achieve great results in reducing Bullwhip Effect. Even so, we have also shown that the inclusion of more advanced forecasting methods (ARIMA models) allows an even better system performance. 

Table 8 is an extension of Table 3 but adding a column with the results when considering the ARIMA models to forecast demand in the first level of the supply chain (BW5). Furthermore, we show the reduction achieved in each case. 

GM (1,1) model based on ARIMA residual error correction is established and the combined model takes advantages of the two kinds of mathematical model, gray forecast model and ARIMA model. Compared with simply using the grey forecasting model, it could improve the prediction accuracy of gas concentration and reduce the relative prediction error. 

ARIMA is a sophisticated univariate modeling technique. ARIMA is the abbreviation of Autoregressive integrated moving average (also known as the Box-Jenkins model). It was developed in 1970 for forecasting purposes and relies solely on the past behavior of the variable being forecasted. The model creates the value of F, with input from previous values of the same dataset. This input includes a factor of previous values as well as the elasticity of the... 

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