> ML_LITERATURE // TAYLOR-2018-FORECASTING-AT-SCALE-PROPHET_v1.0
Forecasting at Scale (Prophet)
Sean J. Taylor, Benjamin Letham · The American Statistician (2018)
algorithm2018industry-standardthirdPartyReproduced
Principal Contribution
Formulated a modular generalized additive model (GAM) combining saturating logistic growth trends, Fourier series seasonality, holiday effects, and change points fit via Stan L-BFGS.
Operational Relevance
Serves as qualified reference for implementing task-time-series-forecasting in production systems.
Assumptions
- Underlying spatio-temporal continuity and domain distributional stability hold
Limitations
- Performance scaling and computational footprint depend on receptive field depth, sequence length, and resolution