Currently, the core and most important parameters are exposed in the configuration UI.
However, some parameters are still missing that might prove useful in certain forecast scenarios. These are:
- Parameter:
growth – has two options:
linear (default)logistic – bounded growth, requires a cap column to be added to the training dataset. Training and forecasting will be significantly slower when this growth type is used.
- Parameter:
interval_width – does not affect the actual accuracy or prediction but allows customization of the interval band for better readability and visualization.
- Parameter:
holidays – allows adding holidays (name, start datetime, end datetime).
- Parameter:
holiday_prior_scale – tunes the impact holidays have on the prediction.
- Low value = less impact unless strongly indicated by the model.
- High value = higher impact.
- Parameter:
regressor.prior_scale – tunes the impact a regressor has on the prediction.
Expose these additional parameters in the UI and add them to the model configuration.
Consider adding a Advanced toggle/switch to the UI to display parameters that, in most cases, do not need to be adjusted.
All parameters:
class Prophet(object):
"""Prophet forecaster.
Parameters
----------
growth: String 'linear', 'logistic' or 'flat' to specify a linear, logistic or
flat trend.
changepoints: List of dates at which to include potential changepoints. If
not specified, potential changepoints are selected automatically.
n_changepoints: Number of potential changepoints to include. Not used
if input `changepoints` is supplied. If `changepoints` is not supplied,
then n_changepoints potential changepoints are selected uniformly from
the first `changepoint_range` proportion of the history.
changepoint_range: Proportion of history in which trend changepoints will
be estimated. Defaults to 0.8 for the first 80%. Not used if
`changepoints` is specified.
yearly_seasonality: Fit yearly seasonality.
Can be 'auto', True, False, or a number of Fourier terms to generate.
weekly_seasonality: Fit weekly seasonality.
Can be 'auto', True, False, or a number of Fourier terms to generate.
daily_seasonality: Fit daily seasonality.
Can be 'auto', True, False, or a number of Fourier terms to generate.
holidays: pd.DataFrame with columns holiday (string) and ds (date type)
and optionally columns lower_window and upper_window which specify a
range of days around the date to be included as holidays.
lower_window=-2 will include 2 days prior to the date as holidays. Also
optionally can have a column prior_scale specifying the prior scale for
that holiday.
seasonality_mode: 'additive' (default) or 'multiplicative'.
seasonality_prior_scale: Parameter modulating the strength of the
seasonality model. Larger values allow the model to fit larger seasonal
fluctuations, smaller values dampen the seasonality. Can be specified
for individual seasonalities using add_seasonality.
holidays_prior_scale: Parameter modulating the strength of the holiday
components model, unless overridden in the holidays input.
changepoint_prior_scale: Parameter modulating the flexibility of the
automatic changepoint selection. Large values will allow many
changepoints, small values will allow few changepoints.
mcmc_samples: Integer, if greater than 0, will do full Bayesian inference
with the specified number of MCMC samples. If 0, will do MAP
estimation.
interval_width: Float, width of the uncertainty intervals provided
for the forecast. If mcmc_samples=0, this will be only the uncertainty
in the trend using the MAP estimate of the extrapolated generative
model. If mcmc.samples>0, this will be integrated over all model
parameters, which will include uncertainty in seasonality.
uncertainty_samples: Number of simulated draws used to estimate
uncertainty intervals. Settings this value to 0 or False will disable
uncertainty estimation and speed up the calculation.
stan_backend: str as defined in StanBackendEnum default: None - will try to
iterate over all available backends and find the working one
holidays_mode: 'additive' or 'multiplicative'. Defaults to seasonality_mode.
"""
Currently, the core and most important parameters are exposed in the configuration UI.
However, some parameters are still missing that might prove useful in certain forecast scenarios. These are:
growth– has two options:linear(default)logistic– bounded growth, requires acapcolumn to be added to the training dataset. Training and forecasting will be significantly slower when this growth type is used.interval_width– does not affect the actual accuracy or prediction but allows customization of the interval band for better readability and visualization.holidays– allows adding holidays (name, start datetime, end datetime).holiday_prior_scale– tunes the impact holidays have on the prediction.regressor.prior_scale– tunes the impact a regressor has on the prediction.Expose these additional parameters in the UI and add them to the model configuration.
Consider adding a Advanced toggle/switch to the UI to display parameters that, in most cases, do not need to be adjusted.
All parameters: