How to validate an ANN model? Currently, I have made an ARIMA model and to validate it I have studied the autocorrelation (ACF and PACF) of residuals and also a Box-Pierce test.
But I am having doubts about how to validate a model made using ARTIFICIAL NEURAL NETWORKS.
Should I treat it as an ARIMA model and study residuals in the same way? Or should I make another consideration?
I was also wondering if the data used as validation has something to do with this (appart from telling me if there is or not overfitting). 
All workship I find on the internet about it doesn't make any explicit validation (they don't assess the residuals or anything).
 A: There are books on the topic.  It is different because these are adaptive systems and so they can change themselves.  While they could be stable currently, it does not mean they will not observe data that makes them unstable.  See the following for examples:
Guidance for the Verification and Validation of Neural Networks by Laura L.
Pullum, Brian J. Taylor, Marjorie A. Darrah 
Independent Verification and Validation of Neural Networks - Developing
Practitioner Assistance By Dr. Laura L. Pullum, Dr. Marjorie A. Darrah, and Mr.
Brian J. Taylor, Institute for Scientific Research, Inc., Software Tech
Toward V&V of neural network based controllers by Johann Schumann and
Stacy Nelson 
Validating A Neural Network-based Online Adaptive System by Yan Liu,
Dissertation submitted to the College of Engineering and Mineral Resources at
West Virginia University, 2005 
Verification and Validation of Adaptive and Intelligent Systems with Flight
Test Results by John Burken and Dick Larson, UCAUV 2009 
A: If you are creating a forecast of a continuous time series, you can do that.
This really depends on your objective though. If you are comparing your network model to the ARIMA, then you will probably want to use a similar metric so you can compare their accuracy. You're not treating it as an ARIMA model if you look at residuals. Residual analysis is widely used, as you probably know from GARCH models.
This said, have you ever heard of gradient boosting? It essentially defines an additive model where each model forecasts the residuals of the previous model's objective. To be clear, the first learner models the series, then the second learner models the first's residuals, and then the third models the residuals of the model of residual left by the second, etc. See article: https://en.wikipedia.org/wiki/Gradient_boosting and talk by Trevor Hastie from Stanford and H20.ai: https://www.youtube.com/watch?v=wPqtzj5VZus
It is commonly used for decision trees, but I am working with logistic boosting (something else) for neural networks in my project.
That was a slight digression though since the question was only about residual analysis. You should play around and see what you can come up with it. Maybe try clustering the residuals and see what happens. That's your choice and your exploration. 
But you want to know about what you're dealing with. What are the limitations and assumptions in looking at residuals only? Are there any in this case? It's your data. You need to know it well. 
Best of luck.
