Probabilistic Models for Time-Series
As mentioned in the introduction, probabilistic models can help us make decisions under uncertainty, and in situations where estimates have to come with quantified confidence, such as in financial forecasting, this can be crucial. For predictions of sales or cash flow, attaching probabilities to model predictions can make it easier for financial controllers and managers to act on the new information.
Some well-known algorithms include Prophet, explicitly designed for monitoring operational metrics and key performance indicators (KPIs), and Markov models. Others are stochastic deep learning models such as DeepAR and DeepState. Since we are dealing with deep learning models in Chapter 10, Deep Learning Models, we'll not deal with them in detail in this chapter.
The Prophet model comes from Facebook (Taylor and Letham, 2017) and is based on a decomposable model with interpretable parameters. A guiding design principle was that...