The second half of the chapter is all about neural networks. In the first part, we will be building a simple neural network that only forecasts the next time step. Since the spikes in the series are very large, we will be working with log-transformed page views in input and output. We can use the short-term forecast neural network to make longer-term forecasts, too, by feeding its predictions back into the network.
Before we can dive in and start building forecast models, we need to do some preprocessing and feature engineering. The advantage of neural networks is that they can take in both a high number of features in addition to very high-dimensional data. The disadvantage is that we have to be careful about what features we input. Remember how we discussed look-ahead bias earlier in the chapter, including future data that would not have been available at the time of forecasting, which is a problem in backtesting.