Probabilistic Models for Time-Series
Probability is a measure of how likely something is to occur. In sales forecasting, an estimate of uncertainty is crucial because these forecasts, providing insights into cash flow, margin, and revenue, drive business decisions on which depend the financial stability and the livelihoods of employees. This is where probabilistic models for time-series come in. They help us make decisions when an estimate of certainty is important.
In this chapter, I'll introduce Prophet, Markov models, and Fuzzy time-series models. At the end, we'll go through an applied exercise with these methods.
Another application for probabilistic modeling is estimating counterfactuals, where we can estimate treatment effects in experiments. We'll discuss the concept of Bayesian Structural Time-Series Models, and we'll run through a practical example with a time-series in the practice section.
We're going to cover the following topics...
