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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.

    SARIMA differs from ARIMA in seasonal component.
    If your time series has a seasonal component then the values of p,d,q can be different
    for the period of the seasonality.

    ARIMA(p,d,q)*ARIMA(P,D,Q)S
    where p = non-seasonal AR order, d = non-seasonal differencing, q = non-seasonal MA order,
    P = seasonal AR order, D = seasonal differencing, Q = seasonal MA order,
    and S = time span of repeating seasonal pattern.

    Best answer

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