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. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Tucker S. McElroy, James A. Livsey: Ecce Signum: An R Package for Multivariate Signal Extraction and Time Series Analysis (2022) arXiv
- Julien Siebert, Janek Groß, Christof Schroth: A systematic review of Python packages for time series analysis (2021) arXiv
- Michał Narajewski, Jens Kley-Holsteg, Florian Ziel: tsrobprep - an R package for robust preprocessing of time series data (2021) arXiv
- Wick, Felix; Kerzel, Ulrich; Hahn, Martin; Wolf, Moritz; Singhal, Trapti; Stemmer, Daniel; Ernst, Jakob; Feindt, Michael: Demand forecasting of individual probability density functions with machine learning (2021)
- Zain, Zuhaira M.; Alturki, Nazik M.: COVID-19 pandemic forecasting using CNN-LSTM: a hybrid approach (2021)
- Navon, Aviv; Rosset, Saharon: Capturing between-tasks covariance and similarities using multivariate linear mixed models (2020)