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I am a recent graduate working as a Data Scientist and I often get lost in 'choosing' which model/models to use for a predictive task.

Just recently I was trying to build a prediction on time-series and the number of different models/methods available such as ARIMA, SARIMA, ARIMAX etc is pretty large. Not only that, but even though I can read more about them and have an 'educated guess' at which one to use; I still do not know the assumptions behind the models and whether they are suitable for a task at all. It is like a try and see if it works method.

So I am looking for some books/reference mostly concerned with traditional statistical inference models I can dive deep into and learn as much as I can about it. (I have a background in Mathematics and Statistics)

Thank you

MilTom
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  • You need to make it more precise, since in this form any book on machine learning and statistical models would fall into this category. – Tim Jul 13 '20 at 18:46
  • Traditional time-series statistical models (not ML). I think I am looking at discrete models as from my understanding the continuous models are stochastic analysis territory. – MilTom Jul 13 '20 at 20:55
  • Than it was ambiguous, since non-time-series models also make predictions. Nonetheless, this seems to be answered in several places already, so check the linked threads. – Tim Jul 13 '20 at 21:06

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