Prophet is an open source time series forecasting algorithm designed by Facebook for ease of use without any expert knowledge in statistics or time series forecasting. Prophet builds a model by finding a best smooth line which can be represented as a sum of the following components:

y(t) = g(t) + s(t) + h(t) + ϵₜ

• Overall growth trend. g(t)
• Yearly seasonality. s(t)
• Weekly seasonality. s(t)
• Holidays effects h(t)

In this series of blog posts, we will see some of the useful functions present in the library **fbprophet**listed below with an example.

1. **Prophet.fit**
2. **Prophet.predict**
3. **Prophet.plot**
4. **Prophet.plot_components**
5. **Prophet.add_seasonality**
6. **Prophet.add_regressors**
7. **Prophet.seasonalities**
8. **Prophet.predictive_samples**

Let’s start by describing the sample data set that we will be using for our demonstration.

#forecasting #python #time-series-forecasting #fbprophet #data-science