Probabilistic Time Series Forecasting

In the preceding chapters, we delved into time series problems from a point forecasting perspective. Point forecasting models predict a single value. However, forecasts are inherently uncertain, so it makes sense to quantify the uncertainty around a prediction. This is the goal of probabilistic forecasting, which can be a valuable approach for better-informed decision-making.

In this chapter, we’ll focus on three types of probabilistic forecasting settings. We’ll delve into exceedance probability forecasting, which helps us estimate the likelihood of a time series surpassing a predefined threshold. We will also deal with prediction intervals, which provide a range of possible values within which a future observation is likely to fall. Finally, we will explore predicted probability forecasting, which offers a probabilistic assessment of individual outcomes, providing a fine-grained perspective of future possibilities.

This chapter...