Prophet Python

The software is good for “the business forecast tasks we have encountered at Facebook” and that, according to the site, means hourly, daily or weekly observations with strong multiple seasonalities. In addition, Prophet is designed to deal with holidays known in advance, missing observations and large outliers.

Prophet’s File Generate feature helps your team track and update quotes or forms . If you or your team find yourselves constantly editing Word or Excel documents to send out to clients Prophet’s File Generation feature can make creating those files as easy as clicking a button.

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.