My question is why do we have to choose the first lags in any time series forecasting?

My question can be answered by the autoregression but the answer I am looking for is a rational answer. I will explain that in few words. For example, the correlation between the current and the lags in the daily forecasting could be high but this is not the case in the monthly (bearing in mind the seasonal cycles) or in the annual time series. Mostly the AR and MA are done according to the ACF and PACF which are linear correlation while the relation is not linear at all taking the streamflow as an example. I do know there is a seasonal AR and so on. But my question is can we have another method? another type of forecasting? of course data-driven methods are included as they mostly being implemented according to the AR in general.

Does the rainfall of the current day has any relation (Markov) with the previous days?? of course no! So, doing that for the daily rainfall is completely wrong but it can be applied for streamflow for example.

Again the question is why do we choose first lags and not only the seasonal lags (i.e. one year cycle) for example?

Thanks

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