What is Prophet Modelling?

It is open for collaboration, and people are free to make pull requests to improve it further . One major advantage with Prophet is that it does not require much prior knowledge of forecasting time series data as it can automatically find seasonal trends with a set of data and offers easy to understand parameters.

To use Prophet for forecasting, first, a Prophet() object is defined and configured, then it is fit on the dataset by calling the fit() function and passing the data . The Prophet() object takes arguments to configure the type of model you want, such as the type of growth, the type of seasonality, and more.26 Ağu 2020

Prophet includes functionality for time series cross validation to measure forecast error using historical data. This is done by selecting cutoff points in the history, and for each of them fitting the model using data only up to that cutoff point . We can then compare the forecasted values to the actual values.

Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects . It works best with time series that have strong seasonal effects and several seasons of historical data.

At its core, the Prophet procedure is an additive regression model with four main components: A piecewise linear or logistic growth curve trend. Prophet automatically detects changes in trends by selecting changepoints from the data. A yearly seasonal component modeled using Fourier series.