Summary

In this chapter, we explored different techniques for generating probabilistic forecasts like Probability Density Functions, Quantile functions, Monte Carlo Dropout, and Conformal Prediction. We deep-dived into each of them and learned how to apply them to real data. For Conformal Predictions, an actively researched field, we learned different ways to conformalize prediction intervals with different underlying mechanisms, like Conformalized Quantile Regression, conformalizing uncertainty estimates, and so on. And finally, we saw a few tweaks to make conformal prediction work even better for time series problems.

To wrap it up, we also looked at a few topics that are on the road less traveled, like intermittent demand forecasting, interpretability, cold-start forecasting, and hierarchical forecasting.

In the next part of this book, we will look at a few mechanics of forecasting, such as multi-step forecasting, cross-validation, and evaluation.