# Summary

In this chapter, we've discussed how probabilistic models for time-series can help us make decisions with an estimate of uncertainty in the context of financial forecasting. These forecasts drive business decisions for financial planning.

I've introduced Prophet, Markov models, and Fuzzy time-series models. We've discussed the components of Facebook's Prophet model. For Markov models, we've discussed the main ideas, such as the Markov property, and we've discussed more details regarding Switching Models. Then I've explained some basics of fuzzy set theory and how this is applied to time-series.

Finally, we've delved into the intuition and some of the theory of BSTS models in the context of estimating treatment effects in experiments.

Finally, we went through an applied exercise with each method. In the BSTS practice, we've looked at the effect of the Volkswagen emissions scandal.