Automatic Forecasting Procedure
Plot the components of a prophet forecast. Prints a ggplot2 with which...
Prophet forecaster.
Summarise the coefficients of the extra regressors used in the model. ...
Dataframe indicating which columns of the feature matrix correspond to...
Root mean squared error
Compute a rolling mean of x, after first aggregating by h
Compute a rolling median of x, after first aggregating by h
Simulate observations from the extrapolated generative model.
Prophet posterior predictive samples.
Simulate the trend using the extrapolated generative model.
Prepare dataframe for plotting seasonal components.
Set seasonalities that were left on auto.
Set changepoints
Convert date vector
Prepare dataframe for fitting or predicting.
Forecast for a single cutoff. Used in cross_validation function when e...
Symmetric mean absolute percentage error based on Chen and Yang (2004)...
Time difference between datetimes
Validates the name of a seasonality, holiday, or regressor.
Validates the inputs to Prophet.
Get layers to overlay significant changepoints on prophet forecast plo...
Add in built-in holidays for the specified country.
Adds a component with given name that contains all of the components i...
Add an additional regressor to be used for fitting and predicting.
Generate cutoff dates
Generated table of holiday dates at the country level from 1995 to 204...
Return all possible holiday names of given country
Get the stan backend defined in the environment variables.
Initialize model scales.
Initialize constant growth.
Initialize logistic growth.
Mean absolute error
Dataframe with seasonality features. Includes seasonality features, ho...
Make dataframe with future dates for forecasting.
Construct a matrix of holiday features.
Make dataframe of holidays for given years and countries
Data frame with seasonality features.
Mean absolute percent error
Median absolute percent error
Mean squared error
Get number of Fourier components for built-in seasonalities.
Compute performance metrics from cross-validation results.
Evaluate the piecewise linear function.
Evaluate the piecewise logistic function.
Plot a performance metric vs. forecast horizon from cross validation. ...
Plot a particular component of the forecast.
Plot a custom seasonal component.
Plot the weekly component of the forecast.
Plot the yearly component of the forecast.
Plot the prophet forecast.
Predict seasonality components, holidays, and added regressors.
Predict trend using the prophet model.
Prophet uncertainty intervals for yhat and trend
Predict using the prophet model.
Sample from the posterior predictive distribution.
Copy Prophet object.
Plot the prophet forecast.
Fit the prophet model.
Initialize flat growth.
Evaluate the flat trend function.
Add a seasonal component with specified period, number of Fourier comp...
Check that the required packages for using the cmdstanr backend are in...
Construct a dataframe of holiday dates.
Coverage
Cross-validation for time series.
Merge history and forecast for plotting.
Obtain the point estimates of the parameters of the Prophet model usin...
Load the Prophet Stan model.
Obtain the joint posterior distribution of the parameters of the Proph...
Gives Stan arguments the appropriate names depending on the chosen Sta...
Provides Fourier series components with the specified frequency and or...
Implements 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.