Summary

In this chapter, you learned about a wide range of conventional tools for dealing with time series data. You also learned about one-dimensional convolution and recurrent architectures, and finally, you learned a simple way to get your models to express uncertainty.

Time series are the most iconic form of financial data. This chapter has given you a rich toolbox for dealing with time series. Let's recap all of the things that we've covered on the example of forecasting web traffic for Wikipedia:

• Basic data exploration to understand what we are dealing with

• Fourier transformation and autocorrelation as tools for feature engineering and understanding data

• Using a simple median forecast as a baseline and sanity check

• Understanding and using ARIMA and Kalman filters as classic prediction models

• Designing features, including building a data loading mechanism for all our time series

• Using one-dimensional convolutions and variants such as causal convolutions and dilated convolutions

• Understanding...