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...
