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I was wondering if there was a good paper out there that informs about model and data assumptions in AI/ML approaches.

For example, if you look at Time Series Modelling (Estimation or Prediction) with Linear models or (G)ARCH/ARMA processes, there are a lot of data assumptions that have to be satisfied to meet the underlying model assumptions:

**Linear Regression**

- No Autocorrelation in your observations, often when dealt with level data (--> ACF)
- Stationarity (Unit-Roots --> Spurious Regressions)
- Homoscedasticity
- Assumptions about error term distribution "Normaldist" (mean = 0, and some finite variance) etc.

**Autoregressive Models**

- stationarity
- squared error autocorrelation
- ...

When dealing with ML/AI approaches, it feels like you can throw whatever you like as an input (my subjective perception). You are satisfied with the result as long as some prediction/estimation error measurement is good enough (similar to a high, but often misleading R²).

**What assumptions have to be satisfied for an RNN, CNN or LSTM model that find application in time-series prediction?**

Any thoughts?

**ADDED**

- Good Article describing my question/thoughts.
- Medium Article discussing model assumptions + tests, but not in the context of more advanced models
- I read the 100-page ML Book- Unfortunately almost no content about model assumptions or how to test for them.

Well deserved bounty! Thanks for the answer. – Maeaex1 – 2019-12-17T16:29:42.080