This is a blind/dummy (no assumption whatsoever) application of Prophet automatic procedure for forecast estimates of Eurostat tour_occ_nim time-series on the number of "nights spent at tourist accommodation establishments" per month.
Description
(from the source webpage)
At its core, the Prophet procedure is an additive regression model with four main components (using Stan Bayesian approach, see reference below):
- 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,
- a weekly seasonal component using dummy variables,
- a user-provided list of important holidays.
In practice, non-linear trends are fit with yearly and weekly seasonality (plus holidays). The method is also robust to missing data, shifts in the trend, and large outliers.
Usage
Facebook has open sourced Prophet software, a forecasting project with an interface available in Python. We use this resource.
Run the tour_forecast.py source code or explore the run_forecast.ipynb notebook to produce the following 5-years forecast estimates of Eurostat tour_occ_nim monthly indicator:
Another example is provided by the 1-year prediction of unemployment une_rt_m monthly indicator:
About
| status | since 2017 – closed |
| contributors |
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| license | EUPL |
- Taylor, S.J. and Letham, B. (2017): Forecasting at Scale.