WeeklyFourier

pydantic model pymc_marketing.mmm.fourier.WeeklyFourier

Weekly fourier seasonality.

(Source code, png, hires.png, pdf)

n_orderint

Number of fourier modes to use.

prefixstr, optional

Alternative prefix for the fourier seasonality, by default None or “fourier”

priorPrior | VariableFactory, optional

Prior distribution or VariableFactory for the fourier seasonality beta parameters, by default Prior("Laplace", mu=0, b=1)

namestr, optional

Name of the variable that multiplies the fourier modes, by default None

variable_namestr, optional

Name of the variable that multiplies the fourier modes, by default None

Methods

WeeklyFourier.__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
WeeklyFourier.apply(dayofperiod[, sum])	Apply fourier seasonality to day of year.
WeeklyFourier.from_dict(data)	Deserialize the Fourier seasonality.
WeeklyFourier.get_default_start_date([...])	Get the start date for the Fourier curve.
WeeklyFourier.plot_curve(curve[, n_samples, ...])	Plot the seasonality for one full period.
WeeklyFourier.plot_curve_hdi(curve[, ...])	Plot full period of the fourier seasonality.
WeeklyFourier.plot_curve_samples(curve[, n, ...])	Plot samples from the curve.
WeeklyFourier.sample_curve(parameters[, ...])	Create full period of the Fourier seasonality.
WeeklyFourier.sample_prior([coords])	Sample the prior distributions.
WeeklyFourier.serialize_prior()	Serialize the prior distribution.
WeeklyFourier.to_dict([_orig])	Serialize the Fourier seasonality.

field days_in_period: float = 7

field n_order: int [Required]

Constraints:

• gt = 0

field prefix: str = 'fourier'

field prior: InstanceOf[Prior] | InstanceOf[VariableFactory] = Prior("Laplace", mu=0, b=1)

field variable_name: str | None = None