吴恩达-《序列模型》课程

时间序列-先知模型 (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. At its core, it is an additive regressive model which can detect change points to model the time series.

2017年，Facebook开源了先知模型，该模型能够在日水平，周水平，年水平等和趋势方面对具有多个季节性的时间序列进行建模。 它具有直观的参数，不是那么专业的数据科学家可以调整这些参数以获得更好的预测。 它的核心是可加回归模型，可以检测变化点以对时间序列建模。

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

先知将时间序列分解为趋势$ g_ {t} $，季节性$ S_ {t} $和假期$ h_ {t} $的分量。

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

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

Where, $\epsilon_{t}$ is the error term.

其中， \\ epsilon_ {t} $是错误项。

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

谷歌和推特分别在R中引入了类似的时间序列预测软件包，例如因果影响和异常检测。

翻译自: https://www.tutorialspoint.com/time_series/time_series_prophet_model.htm

吴恩达-《序列模型》课程