Time Series - Prophet Model

In 2017, Facebook open sourced the prophet model which was capable of modelling the time series with strong multiple seasonalities at day level, week level, year level etc. and trend. It has intuitive parameters that a not-so-expert data scientist can tune for better forecasts.

Time Series - Prophet Model

In 2017, Facebook open-sourced the prophet model which was capable of modeling the time series with strong multiple seasonalities at the day level, week level, year level, etc. and trend. It has intuitive parameters that a not-so-expert data scientist can tune for better forecasts. At its core, it is an additive regressive model that can detect change points to model the time series.

Prophet decomposes the time series into components of trend  gt , seasonality  S and holidays

ht .

                                                                                        yt = gt + st + ht + ϵt

Where,                                ϵt   is the error term.

Similar packages for time series forecasting such as causal impact and anomaly detection were introduced in R by google and twitter respectively.