# Forecasting Foreign Exchange with Neural Network - Lag in Prediction

I have a question regarding the use of neural network. I am currently working with R (neuralnet package) and I am facing the following issue. My testing and validation set are always late with respect to the historical data. Is there a way of correcting the result? Maybe something is wrong in my analysis

1. I use the daily log return
2. I normalise my data with the sigmoid function (sigma and mu computed on my whole set)
3. I train my neural networks with 10 dates and the output is the normalised value that follows these 10 dates.

I tried to add the trend but there is no improvement, I observed 1-2 days late. My process seems ok, what do you think about it?

1. Consider a different normalization: The sigmoid function will attenuate large moves. It is likely precisely these large non-linear moves that attracted you to using neural networks in the first place. Why remove them? A simple whitening of the data may be better