The last problem we're going to see in this chapter is about prediction in time. The standard name for these problems is time series analysis, since the prediction is made on descriptors extracted in the past; therefore, the outcome at the current time will become a feature for the prediction of the next point in time. In this exercise, we're using the closing values for several stocks composing the Dow Jones index in 2011.

Several features compose the dataset, but in this problem (to make a short and complete exercise) we're just using the closing values of each week for each of the 30 measured stocks, ordered in time. The dataset spans six months: we're using the first half of the dataset (corresponding to the first quarter of the year under observation, with 12 weeks) to train our algorithm, and the second half (containing the second quarter of the year, with 13 weeks) to test the predictions.

Moreover, since we don't expect readers to have a background in economics...