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  1. swaplaw007
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    The ARIMA model is ARIMA(p,d,q)
    where p = no of previous lags which are significant in determining present value.
    d = The order of differencing you need to do to make the time series stationary.
    q = The no of error terms in the previous lags which are significant in determining the present value.

    The ARIMAX model differs from ARIMA in the X component which stands for exogenous variables.
    Exogenous stands for having an external cause or origin.

    While forecasting a particular time series there can be external factors like unemployment, inflation,
    natural calamity,political change which affects the value of time series. The ARIMAX model takes these
    factors into consideration while forecasting.

    To give a perfect example, if you are forecasting stock prices for any company at present.
    When the covid-19 crisis struck the world, the value of many stocks dipped. Now, this was unprecedented
    and the value of the stocks did not depend on how the prices varied during some past couple of years
    but the virus played a critical role in affecting the prices of the stock.

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