# R - Interpreting neural networks plot

I know there are similar question on stats.SE, but I didn't find one that fulfills my request; please, before mark the question as a duplicate, ping me in the comment.

I run a neural network based on neuralnet to forecast SP500 index time series and I want to understand how I can interpret the plot posted below:

Particularly, I'm interested to understand what is the interpretation of the hidden layer weight and the input weight; could someone explain me how to interpret that number, please?

Any hint will be appreciated.

• Here is an interesting article that relates to your question. labs.eeb.utoronto.ca/jackson/ecol.%20modelling%20ANN.pdf – MrMeritology Jul 10 '15 at 4:06
• Thanks for the comment, @MrMeritology! I found that really useful! – Quantopik Jul 10 '15 at 11:25
• While I'm sure you you'll be able to understand this (pretty simple) neural network, if interpretability is a relatively large concern then you probably shouldn't be using a neural network in the first place. Is there a specific reason you selected one over other algorithms? – David Jul 11 '15 at 0:06
• Yes, @David! I would like to learn using this kind of model. I never used them in my job and I'm studying that just for fun. Do you have any idea about interpreting the plot? – Quantopik Jul 11 '15 at 9:27
• i need help to interpret an ANN analysis Anybody that can help? – bright kalu Oct 31 '18 at 19:29

• Great answer @cdeterman! many things seem to be more clear now. last thing... according to you, how can I backtest the model ability to forecast the output basing on the input value. Let me explain better; in the case I use a simple logistic model, I can use the $\beta$ vector and the independent variables to compute a forecast of the dependent variable. In the neural network model, how can I do this? – Quantopik Jan 27 '16 at 18:36
• @Quantopic I think you are referring to the compute function in the neuralnet package. The source isn't terribly complex if you wish to do it by hand. Essentially you are applying the weights and activation function at each layer to the final result. – cdeterman Jan 27 '16 at 18:48