I was wondering if there is 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.

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.

I was wondering if there is 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).

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.

I was wondering if there is 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.

I was wondering if there is 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).

Any thoughts?

ADDED

Good Article describing my question/thoughts.

I was wondering if there is 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).

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.