Poor auto-ARIMA predictions I am trying to fit and forecast water production in a well and this accounts for my end of training thesis. But I got poor prediction from ARIMA and sarima models. I tried with auto ARIMA but it didn't go better. I am trying now to modify the sarima parameters to obtain better results, but it's very tedious.



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 A: *

*Your autocorrelation has a sinusoidal shape with peaks and troughs at lags 7 and 14. It looks like you have daily data with weekly seasonality, which looks strange for water production (Mother Nature does not really work at weekly granularities), but would make perfect sense for water demand (different patterns of demand during weekdays vs. weekends). If the latter, it may make sense to look at seasonal ARIMA.
We don't know whether you specified the frequency in your data and auto_arima decided not to use a seasonal model (which can be a perfectly valid decision if the seasonal pattern is too weak to detect in your series), or whether you didn't, in which case auto_arima can't on its own decide which seasonal frequency to use. See here. You can force auto_arima to use a seasonal model, but this is not guaranteed to improve your forecasts.


*As Galen mentions, your data does not exhibit any obvious patterns. In such a situation, a flat forecast may actually be the best forecast, possibly even an overall historical mean forecast.


*The first thing that jumps out at you in your time series is the one large positive peak and the three large negative ones. If you want to forecast, you should try to understand what happened here and use appropriate predictors, running a regression with (potentially) ARIMA errors. Understanding key drivers is always more important to forecasting than fiddling around with ARIMA orders. Related: How to know that your machine learning problem is hopeless?
