Solved – Implementing Neural Network for time series

I am currently working on neural networks for time series forecasting. My doubt is: do we need to take into account issues like trend, non-stationarity and seasonality while using neural networks instead of the box jenkins methodology?

If yes, how do we do that? For instance, do we need to change the cost function or other parameters? I am currently using nnet and neuralnet packages in R but I do not find anything about these issues in the related documentation.

If not, then does it imply that the model learns the seasonality from data itself? For instance, suppose there is a time series with an upward trend, along with some variations. Could I assume that if we fit a neural network on such series then it might learn just an interpolation?

Some Googling for specifically neural networks and seasonality leads to this paper, Neural network forecasting for seasonal and trend time series, Zhang and Qi, European Journal of Operational Research, V.160, 2, 16 January 2005, 501–514. In this paper the authors sought to compare the Box-Jenkins approach with a neural network approach. From the abstract:

We find that neural networks are not able to capture seasonal or trend variations effectively with the unpreprocessed raw data and either detrending or deseasonalization can dramatically reduce forecasting errors. Moreover, a combined detrending and deseasonalization is found to be the most effective data preprocessing approach.

They conclude that accounting for trend and seasonality in your preprocessing steps is a good idea.

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