statsmodels.tsa.deterministic.CalendarSeasonality¶

class statsmodels.tsa.deterministic.CalendarSeasonality(freq: str, period: str)[source]¶

Seasonal dummy deterministic terms based on calendar time

Parameters

freqstr: The frequency of the seasonal effect.
periodstr: The pandas frequency string describing the full period.

See also

DeterministicProcess
CalendarTimeTrend
CalendarFourier
Seasonality

Examples

Here we simulate irregularly spaced data (in time) and hourly seasonal dummies for the data.

>>> import numpy as np
>>> import pandas as pd
>>> base = pd.Timestamp("2020-1-1")
>>> gen = np.random.default_rng()
>>> gaps = np.cumsum(gen.integers(0, 1800, size=1000))
>>> times = [base + pd.Timedelta(gap, unit="s") for gap in gaps]
>>> index = pd.DatetimeIndex(pd.to_datetime(times))

>>> from statsmodels.tsa.deterministic import CalendarSeasonality
>>> cal_seas_gen = CalendarSeasonality("H", "D")
>>> cal_seas_gen.in_sample(index)

Attributes

freq: The frequency of the deterministic terms
is_dummy: Flag indicating whether the values produced are dummy variables
period: The full period

Methods

`in_sample`(index)	Produce deterministic trends for in-sample fitting.
`out_of_sample`(steps, index[, forecast_index])	Produce deterministic trends for out-of-sample forecasts

Methods

`in_sample`(index)	Produce deterministic trends for in-sample fitting.
`out_of_sample`(steps, index[, forecast_index])	Produce deterministic trends for out-of-sample forecasts

Properties

`freq`	The frequency of the deterministic terms
`is_dummy`	Flag indicating whether the values produced are dummy variables
`period`	The full period