statsmodels.tsa.deterministic.CalendarTimeTrend¶

class statsmodels.tsa.deterministic.CalendarTimeTrend(freq, constant=True, order=0, *, base_period=None)[source]¶

Constant and time trend determinstic terms based on calendar time

Parameters:¶

freqstr

A string convertible to a pandas frequency.

constantbool

Flag indicating whether a constant should be included.

orderint

A non-negative int containing the powers to include (1, 2, …, order).

base_period{str, pd.Timestamp}, default None

The base period to use when computing the time stamps. This value is treated as 1 and so all other time indices are defined as the number of periods since or before this time stamp. If not provided, defaults to pandas base period for a PeriodIndex.

Attributes:¶

base_period

The base period

constant

Flag indicating that a constant is included

freq

The frequency of the deterministic terms

is_dummy

Flag indicating whether the values produced are dummy variables

order

Order of the time trend

See also

DeterministicProcess

CalendarFourier

CalendarSeasonality

TimeTrend

Notes

The time stamp, \(\tau_t\), is the number of periods that have elapsed since the base_period. \(\tau_t\) may be fractional.

Examples

Here we simulate irregularly spaced hourly data and construct the calendar time trend terms 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 CalendarTimeTrend
>>> cal_trend_gen = CalendarTimeTrend("D", True, order=1)
>>> cal_trend_gen.in_sample(index)

Next, we normalize using the first time stamp

>>> cal_trend_gen = CalendarTimeTrend("D", True, order=1,
...                                   base_period=index[0])
>>> cal_trend_gen.in_sample(index)

Methods

from_string(freq, trend[, base_period])	Create a TimeTrend from a string description.
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

base_period	The base period
constant	Flag indicating that a constant is included
freq	The frequency of the deterministic terms
is_dummy	Flag indicating whether the values produced are dummy variables
order	Order of the time trend