When deciding between ARIMA and linear regression for forecasting, the main consideration is the nature of the data.
ARIMA models are specifically designed for time-series data, which have a temporal ordering and a potential dependence on past observations. ARIMA models are used to model the underlying structure of time-series data, capturing trends, seasonal patterns, and other patterns in the data. ARIMA models are particularly useful when there is evidence of autocorrelation in the data, meaning that past values of the series are correlated with future values.
Linear regression, on the other hand, is generally used for modeling the relationship between a dependent variable and one or more independent variables. It assumes that the relationship between the variables is linear and that there is a constant variance of the errors. Linear regression is often used to make predictions or forecasts when there is a clear relationship between the dependent variable and one or more independent variables.
Therefore, if the data being analyzed is time-series data, then an ARIMA model may be more appropriate for forecasting than linear regression. However, if there is a clear linear relationship between the dependent variable and one or more independent variables, then linear regression may be more appropriate.
It is worth noting that in some cases, both ARIMA and linear regression may be used together for forecasting. For example, a linear regression model may be used to model the relationship between the dependent variable and one or more independent variables, and the residuals from the regression model may then be analyzed using an ARIMA model to capture any remaining autocorrelation.
In summary, when deciding between ARIMA and linear regression for forecasting, consider the nature of the data and the relationship between the dependent variable and any independent variables. If the data is time-series data and there is evidence of autocorrelation, then an ARIMA model may be more appropriate. If there is a clear linear relationship between the dependent variable and one or more independent variables, then linear regression may be more appropriate.