Prophet multivariate time series

Prophet will by default fit weekly and yearly seasonalities, if the time series is more than two cycles long. It will also fit daily seasonality for a sub-daily time series . You can add other seasonalities (monthly, quarterly, hourly) using the add_seasonality method (Python) or function (R).

When forecasting, I like to use 1 – weighted MAPE as an error metric to determine the fit of the model . I take the absolute error of the actual – predicted values at a daily level the aggregate all the errors up and divide by the total sales. For the Store 1 model, it has a forecast accuracy of 94.79%.27 Tem 2021

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.

What is Facebook Prophet and how does it work? Facebook Prophet is an open-source algorithm for generating time-series models that uses a few old ideas with some new twists . It is particularly good at modeling time series that have multiple seasonalities and doesn’t face some of the above drawbacks of other algorithms.19 Şub 2021

The Prophet algorithm is an additive model , which means that it detects the following trend and seasonality from the data first, then combine them together to get the forecasted values.