regressor_coefficients

<p>Prophet model object, after fitting.</p>

<p>Coefficients are measured on the original scale of the training data.</p>

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.

Sean Taylor

prophet

Automatic Forecasting Procedure

Ben Letham

regressor_coefficients function

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

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

Description

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Details