def test_deterministic(reset_randomstate):
y = pd.Series(np.random.normal(size=200))
terms = [TimeTrend(constant=True, order=1), Seasonality(12)]
dp = DeterministicProcess(y.index, additional_terms=terms)
m = AutoReg(y, trend="n", seasonal=False, lags=2, deterministic=dp)
res = m.fit()
m2 = AutoReg(y,
trend="ct",
seasonal=True,
lags=2,
period=12,
old_names=False)
res2 = m2.fit()
assert_almost_equal(np.asarray(res.params), np.asarray(res2.params))
with pytest.warns(RuntimeWarning, match="When using deterministic, trend"):
AutoReg(y, trend="ct", seasonal=False, lags=2, deterministic=dp)
def test_time_trend_smoke(index, forecast_index):
tt = TimeTrend(True, 2)
tt.in_sample(index)
steps = 83 if forecast_index is None else len(forecast_index)
warn = None
if type(index) is NumericIndex and np.any(np.diff(index) != 1):
warn = UserWarning
with pytest.warns(warn):
tt.out_of_sample(steps, index, forecast_index)
str(tt)
hash(tt)
assert isinstance(tt.order, int)
assert isinstance(tt._constant, bool)
assert TimeTrend.from_string("ctt") == tt
assert TimeTrend.from_string("ct") != tt
assert TimeTrend.from_string("t") != tt
assert TimeTrend.from_string("n") != tt
assert Seasonality(12) != tt
tt0 = TimeTrend(False, 0)
tt0.in_sample(index)
str(tt0)
det_proc.range(58, 70)
# ## Advanced Construction
#
# Deterministic processes with features not supported directly through the
# constructor can be created using `additional_terms` which accepts a list
# of `DetermisticTerm`. Here we create a deterministic process with two
# seasonal components: day-of-week with a 5 day period and an annual
# captured through a Fourier component with a period of 365.25 days.
from statsmodels.tsa.deterministic import Fourier, Seasonality, TimeTrend
index = pd.period_range("2020-03-01", freq="D", periods=2 * 365)
tt = TimeTrend(constant=True)
four = Fourier(period=365.25, order=2)
seas = Seasonality(period=7)
det_proc = DeterministicProcess(index, additional_terms=[tt, seas, four])
det_proc.in_sample().head(28)
# ## Custom Deterministic Terms
#
# The `DetermisticTerm` Abstract Base Class is designed to be subclassed
# to help users write custom deterministic terms. We next show two
# examples. The first is a broken time trend that allows a break after a
# fixed number of periods. The second is a "trick" deterministic term that
# allows exogenous data, which is not really a deterministic process, to be
# treated as if was deterministic. This lets use simplify gathering the
# terms needed for forecasting.
#
# These are intended to demonstrate the construction of custom terms. They
# can definitely be improved in terms of input validation.
def test_seasonality_smoke(index, forecast_index):
s = Seasonality(12)
s.in_sample(index)
steps = 83 if forecast_index is None else len(forecast_index)
warn = None
if type(index) is NumericIndex and np.any(np.diff(index) != 1):
warn = UserWarning
with pytest.warns(warn):
s.out_of_sample(steps, index, forecast_index)
assert isinstance(s.period, int)
str(s)
hash(s)
if isinstance(index, (pd.DatetimeIndex, pd.PeriodIndex)) and index.freq:
s = Seasonality.from_index(index)
s.in_sample(index)
s.out_of_sample(steps, index, forecast_index)
Seasonality.from_index(list(index))