kats.models.prophet module

class kats.models.prophet.ProphetModel(data: kats.consts.TimeSeriesData, params: kats.models.prophet.ProphetParams)

Bases: Generic[kats.models.model.ParamsType]

Model class for Prophet

This class provides fit, predict, and plot methods for Prophet model

data

the input time series data as in kats.consts.TimeSeriesData

params

the parameter class definied with ProphetParams

fit(**kwargs) → None

fit Prophet model

Parameters

None. –

Returns

The fitted prophet model object

static get_parameter_search_space() → List[Dict[str, object]]

get default parameter search space for Prophet model

plot()

plot forecasted results from Prophet model

predict(steps, include_history=False, **kwargs) → pandas.core.frame.DataFrame

predict with fitted Prophet model

Parameters

• steps – the steps or length of prediction horizon

• include_history – if include the historical data, default as False

Returns

time, fcst, fcst_lower, and fcst_upper

Return type

The predicted dataframe with following columns

class kats.models.prophet.ProphetParams(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, interval_width=0.8, uncertainty_samples=1000, cap=None, floor=None, custom_seasonalities: Optional[List[Dict]] = None)

Bases: kats.consts.Params

Parameter class for Prophet model

This is the parameter class for prophet model, it contains all necessary parameters as definied in Prophet implementation: https://github.com/facebook/prophet/blob/master/python/prophet/forecaster.py

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.

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.

cap

capacity, provided for logistic growth

floor

floor, the fcst value must be greater than the specified floor

custom_seasonlities

customized seasonalities, dict with keys “name”, “period”, “fourier_order”

validate_params()

validate Prophet parameters

This method validates some key parameters including growth rate and custom_seasonalities.