sktime.forecasting.fbprophet¶
-
class
sktime.forecasting.fbprophet.
Prophet
(freq=None, add_seasonality=None, add_country_holidays=None, growth='linear', changepoints=None, n_changepoints=25, changepoint_range=0.8, yearly_seasonality='auto', weekly_seasonality='auto', daily_seasonality='auto', holidays=None, seasonality_mode='additive', seasonality_prior_scale=10.0, holidays_prior_scale=10.0, changepoint_prior_scale=0.05, mcmc_samples=0, alpha=0.05, uncertainty_samples=1000, stan_backend=None, verbose=0)[source]¶ Bases:
sktime.forecasting.base.adapters._fbprophet._ProphetAdapter
Prophet forecaster by wrapping fbprophet. :param freq: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
#timeseries-offset-aliases
- Parameters
add_seasonality (Dict with args for Prophet.add_seasonality()) –
- Dict can have the following keys/values:
name: string name of the seasonality component. period: float number of days in one period. fourier_order: int number of Fourier components to use. prior_scale: optional float prior scale for this component. mode: optional ‘additive’ or ‘multiplicative’ condition_name: string name of the seasonality condition.
add_country_holidays (Dict with args for Prophet.add_country_holidays()) –
- Dict can have the following keys/values:
country_name: Name of the country, like ‘UnitedStates’ or ‘US’
growth (String 'linear' or 'logistic' to specify a linear or logistic) – 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.
alpha (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
References
https://facebook.github.io/prophet https://github.com/facebook/prophet