prophet

Get layers to overlay significant changepoints on prophet forecast plot.

Add in built-in holidays for the specified country.

Adds a component with given name that contains all of the components in group.

Add an additional regressor to be used for fitting and predicting.

Add a seasonal component with specified period, number of Fourier components, and prior scale.

Construct a dataframe of holiday dates.

Coverage

Cross-validation for time series.

Merge history and forecast for plotting.

Plot the prophet forecast.

Fit the prophet model.

Initialize flat growth.

Evaluate the flat trend function.

Provides Fourier series components with the specified frequency and order.

Generate cutoff dates

holidays table

Return all possible holiday names of given country

Initialize model scales.

Initialize constant growth.

Initialize logistic growth.

Mean absolute error

Dataframe with seasonality features. Includes seasonality features, holiday features, and added regressors.

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 the prophet forecast.

Plot a performance metric vs. forecast horizon from cross validation. Cross validation produces a collection of out-of-sample model predictions that can be compared to actual values, at a range of different horizons (distance from the cutoff). This computes a specified performance metric for each prediction, and aggregated over a rolling window with horizon.

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.

Predict using the prophet model.

Predict seasonality components, holidays, and added regressors.

Predict trend using the prophet model.

Prophet uncertainty intervals for yhat and trend

Sample from the posterior predictive distribution.

Prophet forecaster.

Copy Prophet object.

Plot the components of a prophet forecast. Prints a ggplot2 with whichever are available of: trend, holidays, weekly seasonality, yearly seasonality, and additive and multiplicative extra regressors.

Summarise the coefficients of the extra regressors used in the model. For additive regressors, the coefficient represents the incremental impact on y of a unit increase in the regressor. For multiplicative regressors, the incremental impact is equal to trend(t) multiplied by the coefficient.

Dataframe indicating which columns of the feature matrix correspond to which seasonality/regressor components.

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 evaluating for multiple cutoffs.

Symmetric mean absolute percentage error based on Chen and Yang (2004) formula

Time difference between datetimes

Validates the name of a seasonality, holiday, or regressor.

Validates the inputs to Prophet.