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