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. At its core, it is an additive regressive model which can detect change points to model the time series.

Prophet decomposes the time series into components of trend $g_{t}$, seasonality $S_{t}$ and holidays $h_{t}$.

$$y_{t}=g_{t}+s_{t}+h_{t}+epsilon_{t}$$

Where, $epsilon_{t}$ 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.