In this chapter, we spent most of our time on studying models that describe a time series in terms of the patterns of correlations between different points in time. This approach led us to the ARIMA family of models, which we have seen are highly configurable and have successfully been employed in many real-world problems. There is a diverse array of methods that have been applied to the time series problem and in fact we have seen a few elsewhere in this book as well.

The neural networks that we studied in Chapter 4, Neural Networks, and the hidden Markov models that we saw in Chapter 8, Probabilistic Graphical Models, are two such examples. Sometimes, we can treat a time series as a regression problem, and so techniques from this area can be leveraged too.

One other important class of methods is exponential smoothing. There are two key premises behind methods that use this approach. The first of these is that a time series is usually decomposed into a number of different...