摘要 : The Auto Regressive Moving Average (ARIMA) model deals with only one single time series and did not allow the inclusion of other information in the model and forecasts. One way to solve this predicament is to use regression with A... 展开 The Auto Regressive Moving Average (ARIMA) model deals with only one single time series and did not allow the inclusion of other information in the model and forecasts. One way to solve this predicament is to use regression with ARIMA errors and provides all the advantages of regression with the powerful time series features of an ARIMA model. This study aims to develop a simple linear regression model with ARIMA errors to yearly production of wheat in India for the period of 1960-2016, where explanatory variable represents time. It is observed that our fitted model is more accurate to our data than ARIMA model. 收起