def seasonal_features(cls, freq: str) -> List[TimeFeature]:
offset = to_offset(freq)
if offset.name == "M":
return [MonthOfYearIndex()]
elif offset.name == "W-SUN":
return [WeekOfYearIndex()]
elif offset.name == "D":
return [DayOfWeekIndex()]
elif offset.name == "B": # TODO: check this case
return [DayOfWeekIndex()]
elif offset.name == "H":
return [HourOfDayIndex(), DayOfWeekIndex()]
elif offset.name == "T":
return [
MinuteOfHourIndex(),
HourOfDayIndex(),
]
else:
RuntimeError(f"Unsupported frequency {offset.name}")
return []
def DayOfWeekSeasonalISSM():
return SeasonalityISSM(num_seasons=7, time_feature=DayOfWeekIndex())
@pytest.mark.parametrize(
"feature, index, cardinality",
[
(
MinuteOfHourIndex(),
pd.date_range(
"01-01-2015 00:00:00", periods=60 * 2 * 24, freq="1min"),
60,
),
(
HourOfDayIndex(),
pd.date_range("01-01-2015 00:00:00", periods=14 * 24, freq="1h"),
24,
),
(
DayOfWeekIndex(),
pd.date_range("01-01-2015", periods=365 * 5, freq="D"),
7,
),
(
DayOfMonthIndex(),
pd.date_range("01-01-2015", periods=365 * 5, freq="D"),
31,
),
(
DayOfYearIndex(),
pd.date_range("01-01-2015", periods=365 * 5, freq="D"),
366,
),
(
WeekOfYearIndex(),
def test_composite_issm_h():
issm = CompositeISSM(
seasonal_issms=[
SeasonalityISSM(num_seasons=7, time_feature=DayOfWeekIndex()),
SeasonalityISSM(num_seasons=12, time_feature=MonthOfYearIndex()),
],
add_trend=False,
)
assert issm.latent_dim() == 1 + 7 + 12
assert issm.output_dim() == 1
time_features = issm.time_features()
assert len(time_features) == 3
time_indices = [
pd.date_range("2020-01-01 21:00:00", periods=10, freq="H"),
pd.date_range("2020-01-31 22:00:00", periods=10, freq="H"),
]
features = mx.nd.array(
np.stack(
[
np.stack([f(time_index) for f in time_features], axis=-1)
for time_index in time_indices
],
axis=0,
))
emission_coeff, transition_coeff, innovation_coeff = issm.get_issm_coeff(
features)
season_indices_dow = [[2] * 3 + [3] * 7, [4] * 2 + [5] * 8]
season_indices_moy = [[0] * 10, [0] * 2 + [1] * 8]
for item in range(2):
for time in range(10):
expected_coeff = mx.nd.concat(
mx.nd.ones((1, 1)),
mx.nd.one_hot(mx.nd.array([season_indices_dow[item][time]]),
7),
mx.nd.one_hot(mx.nd.array([season_indices_moy[item][time]]),
12),
dim=-1,
)
sliced_emission_coeff = mx.nd.slice(
emission_coeff,
begin=(item, time, None, None),
end=(item + 1, time + 1, None, None),
)
assert (sliced_emission_coeff == expected_coeff).asnumpy().all()
sliced_transition_coeff = mx.nd.slice(
transition_coeff,
begin=(item, time, None, None),
end=(item + 1, time + 1, None, None),
)
assert ((sliced_transition_coeff == mx.nd.eye(1 + 7 +
12)).asnumpy().all())
sliced_innovation_coeff = mx.nd.slice(
innovation_coeff,
begin=(item, time, None),
end=(item + 1, time + 1, None),
)
assert (sliced_innovation_coeff == expected_coeff).asnumpy().all()