statsmodels.tsa.deterministic.CalendarSeasonality

class statsmodels.tsa.deterministic.CalendarSeasonality(freq, period)[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.

Attributes:
freq

The frequency of the deterministic terms

is_dummy

Flag indicating whether the values produced are dummy variables

period

The full period

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)

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


Last update: Oct 08, 2024