Time Series 简明教程
Time Series - Prophet Model
2017年，Facebook开源了Prophet模型，该模型能够对具有日、周、年等多强季度的时序进行建模，以及对趋势建模。它具有直观的参数，非专业级的数据科学家可以对其进行调整，以获得更好的预测。它的核心是一个加法回归模型，可以检测时序的变化点。
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将时序分解为趋势分量$g_{t}$，季节分量$S_{t}$和节假日分量$h_{t}$。
Prophet decomposes the time series into components of trend $g_{t}$, seasonality $S_{t}$ and holidays $h_{t}$.
$y_{t}=g_{t}s_{t}+h_{t}\epsilon_{t}$
y_{t}=g_{t}s_{t}+h_{t}\epsilon_{t}
其中，$\epsilon_{t}$ 是误差项。
Where, $\epsilon_{t}$ is the error term.
类似的时间序列预测包（例如因果影响和异常检测）分别由谷歌和推特在 R 中引入。
Similar packages for time series forecasting such as causal impact and anomaly detection were introduced in R by google and twitter respectively.