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Feat/improve seasonal decompose #862

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merged 7 commits into from
Mar 21, 2022
Merged

Feat/improve seasonal decompose #862

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277f2d6
added STL decompose method, added tests
gnwhr Mar 18, 2022
1eb59e9
fixed documentation
gnwhr Mar 21, 2022
b6839bd
updated tests and documentation
gnwhr Mar 21, 2022
73ebb33
updated tests
gnwhr Mar 21, 2022
b60376c
now raises an error instad of a warning if method is incompatible wit…
gnwhr Mar 21, 2022
c34bc5f
typo
gnwhr Mar 21, 2022
ca25195
Merge branch 'master' into feat/improve-seasonal-decompose
hrzn Mar 21, 2022
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108 changes: 107 additions & 1 deletion darts/tests/utils/test_statistics.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,12 +5,20 @@
from darts.tests.base_test_class import DartsBaseTestClass
from darts.utils.statistics import (
check_seasonality,
extract_trend_and_seasonality,
granger_causality_tests,
remove_seasonality,
remove_trend,
stationarity_test_adf,
stationarity_test_kpss,
stationarity_tests,
)
from darts.utils.timeseries_generation import constant_timeseries, gaussian_timeseries
from darts.utils.timeseries_generation import (
constant_timeseries,
gaussian_timeseries,
linear_timeseries,
)
from darts.utils.utils import ModelMode, SeasonalityMode


class TimeSeriesTestCase(DartsBaseTestClass):
Expand Down Expand Up @@ -93,3 +101,101 @@ def test_stationarity_tests(self):
self.assertTrue(stationarity_test_kpss(series_3)[1] > 0.05)
self.assertTrue(stationarity_test_adf(series_3)[1] < 0.05)
self.assertTrue(stationarity_tests)


class SeasonalDecomposeTestCase(DartsBaseTestClass):
pd_series = pd.Series(range(50), index=pd.date_range("20130101", "20130219"))
pd_series = pd_series.map(lambda x: np.sin(x * np.pi / 3 + np.pi / 2))
season = TimeSeries.from_series(pd_series)
trend = linear_timeseries(
start_value=1, end_value=10, start=season.start_time(), end=season.end_time()
)
ts = trend + season

def test_extract(self):
# test default (naive) method
calc_trend, _ = extract_trend_and_seasonality(self.ts, freq=6)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

# test default (naive) method additive
calc_trend, _ = extract_trend_and_seasonality(
self.ts, freq=6, model=ModelMode.ADDITIVE
)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

# test STL method
calc_trend, _ = extract_trend_and_seasonality(
self.ts, freq=6, method="STL", model=ModelMode.ADDITIVE
)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

# check if warning is raised
with self.assertWarns(Warning):
calc_trend, _ = extract_trend_and_seasonality(
self.ts, freq=6, method="STL", model=ModelMode.MULTIPLICATIVE
)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

def test_remove_seasonality(self):
# test default (naive) method
calc_trend = remove_seasonality(self.ts, freq=6)
diff = self.trend - calc_trend
self.assertTrue(np.mean(diff.values() ** 2).item() < 0.5)

# test default (naive) method additive
calc_trend = remove_seasonality(self.ts, freq=6, model=SeasonalityMode.ADDITIVE)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

# test STL method
calc_trend = remove_seasonality(
self.ts,
freq=6,
method="STL",
model=SeasonalityMode.ADDITIVE,
low_pass=9,
)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

# check if warning is raised
with self.assertWarns(Warning):
calc_trend = remove_seasonality(
self.ts, freq=6, method="STL", model=SeasonalityMode.MULTIPLICATIVE
)
diff = self.trend - calc_trend
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

def test_remove_trend(self):
# test naive method
calc_season = remove_trend(self.ts, freq=6)
diff = self.season - calc_season
self.assertTrue(np.mean(diff.values() ** 2).item() < 1.5)

# test naive method additive
calc_season = remove_trend(self.ts, freq=6, model=ModelMode.ADDITIVE)
diff = self.season - calc_season
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

# test STL method
calc_season = remove_trend(
self.ts,
freq=6,
method="STL",
model=ModelMode.ADDITIVE,
low_pass=9,
)
diff = self.season - calc_season
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))

# check if warning is raised
with self.assertWarns(Warning):
calc_season = remove_trend(
self.ts, freq=6, method="STL", model=ModelMode.MULTIPLICATIVE
)
diff = self.season - calc_season
self.assertTrue(np.isclose(np.mean(diff.values() ** 2), 0.0))
92 changes: 76 additions & 16 deletions darts/utils/statistics.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
import numpy as np
from scipy.signal import argrelmax
from scipy.stats import norm
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.seasonal import STL, seasonal_decompose
from statsmodels.tsa.stattools import acf, adfuller, grangercausalitytests, kpss, pacf

from darts import TimeSeries
Expand Down Expand Up @@ -125,9 +125,11 @@ def extract_trend_and_seasonality(
ts: TimeSeries,
freq: int = None,
model: Union[SeasonalityMode, ModelMode] = ModelMode.MULTIPLICATIVE,
method: str = "naive",
**kwargs,
) -> Tuple[TimeSeries, TimeSeries]:
"""
Extracts trend and seasonality from a TimeSeries instance using `statsmodels.seasonal_decompose`.
Extracts trend and seasonality from a TimeSeries instance using `statsmodels.tsa`.

Parameters
----------
Expand All @@ -140,11 +142,21 @@ def extract_trend_and_seasonality(
Must be ``from darts import ModelMode, SeasonalityMode`` Enum member.
Either ``MULTIPLICATIVE`` or ``ADDITIVE``.
Defaults ``ModelMode.MULTIPLICATIVE``.

method
The method to be used to decompose the series.
- "naive" : Seasonal decomposition using moving averages [1]_.
- "STL" : Season-Trend decomposition using LOESS [2]_. Only compatible with ``ADDITIVE`` model type.
kwargs
Other keyword arguments are passed down to the decomposition method.
Returns
-------
Tuple[TimeSeries, TimeSeries]
A tuple of (trend, seasonal) time series.

References
-------
.. [1] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.seasonal_decompose.html
.. [2] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.STL.html
"""

ts._assert_univariate()
Expand All @@ -158,9 +170,26 @@ def extract_trend_and_seasonality(
"The model must be either MULTIPLICATIVE or ADDITIVE.",
)

decomp = seasonal_decompose(
ts.pd_series(), period=freq, model=model.value, extrapolate_trend="freq"
)
if method == "naive":

decomp = seasonal_decompose(
ts.pd_series(), period=freq, model=model.value, extrapolate_trend="freq"
)

elif method == "STL":
if model not in [SeasonalityMode.ADDITIVE, ModelMode.ADDITIVE]:
logger.warn(
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shouldn't we raise an error here?

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Yes, would be better. I will change it.

f"Only ADDITIVE model is compatible with the STL method. Changing from {model} to"
f" {type(model).__name__}.ADDITIVE."
)
decomp = STL(
endog=ts.pd_series(),
period=freq,
**kwargs,
).fit()

else:
raise_log(ValueError(f"Unknown value for method: {method}"), logger)

season = TimeSeries.from_times_and_values(ts.time_index, decomp.seasonal)
trend = TimeSeries.from_times_and_values(ts.time_index, decomp.trend)
Expand All @@ -185,7 +214,6 @@ def remove_from_series(
The type of model considered.
Must be `from darts import ModelMode, SeasonalityMode` Enums member.
Either MULTIPLICATIVE or ADDITIVE.

Returns
-------
TimeSeries
Expand Down Expand Up @@ -218,6 +246,8 @@ def remove_seasonality(
ts: TimeSeries,
freq: int = None,
model: SeasonalityMode = SeasonalityMode.MULTIPLICATIVE,
method: str = "naive",
**kwargs,
) -> TimeSeries:
"""
Adjusts the TimeSeries `ts` for a seasonality of order `frequency` using the `model` decomposition.
Expand All @@ -233,25 +263,43 @@ def remove_seasonality(
Must be a `from darts import SeasonalityMode` Enum member.
Either SeasonalityMode.MULTIPLICATIVE or SeasonalityMode.ADDITIVE.
Defaults SeasonalityMode.MULTIPLICATIVE.
Returns
method
The method to be used to decompose the series.
- "naive" : Seasonal decomposition using moving averages [1]_.
- "STL" : Season-Trend decomposition using LOESS [2]_. Only compatible with ``ADDITIVE`` model type.
Defaults to "naive"
kwargs
Other keyword arguments are passed down to the decomposition method.
Returns
-------
TimeSeries
A new TimeSeries instance that corresponds to the seasonality-adjusted 'ts'.
References
-------
.. [1] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.seasonal_decompose.html
.. [2] https://www.statsmodels.org/devel/generated/statsmodels.tsa.seasonal.STL.html
"""

ts._assert_univariate()
raise_if_not(
model is not SeasonalityMode.NONE,
"The model must be either MULTIPLICATIVE or ADDITIVE.",
)

_, seasonality = extract_trend_and_seasonality(ts, freq, model)
if model not in [SeasonalityMode.ADDITIVE, ModelMode.ADDITIVE] and method == "STL":
logger.warn(
f"Only ADDITIVE seasonality is compatible with the STL method. Changing from {model} to"
f" {type(model).__name__}.ADDITIVE."
)
model = SeasonalityMode.ADDITIVE
_, seasonality = extract_trend_and_seasonality(ts, freq, model, method, **kwargs)
new_ts = remove_from_series(ts, seasonality, model)
return new_ts


def remove_trend(
ts: TimeSeries, model: ModelMode = ModelMode.MULTIPLICATIVE
ts: TimeSeries,
model: ModelMode = ModelMode.MULTIPLICATIVE,
method: str = "naive",
**kwargs,
) -> TimeSeries:
"""
Adjusts the TimeSeries `ts` for a trend using the `model` decomposition.
Expand All @@ -262,18 +310,30 @@ def remove_trend(
The TimeSeries to adjust.
model
The type of decomposition to use.
Must be `from darts import ModelMode` Enum member.
Must be a `from darts import ModelMode` Enum member.
Either ModelMode.MULTIPLICATIVE or ModelMode.ADDITIVE.
Defaults to modelMode.MULTIPLICATIVE.
Defaults ModelMode.MULTIPLICATIVE.
method
The method to be used to decompose the series.
- "naive" : Seasonal decomposition using moving averages [1]_.
- "STL" : Season-Trend decomposition using LOESS [2]_. Only compatible with ``ADDITIVE`` model type.
Defaults to "naive"
kwargs
Other keyword arguments are passed down to the decomposition method.
Returns
-------
TimeSeries
A new TimeSeries instance that corresponds to the trend-adjusted 'ts'.
"""

ts._assert_univariate()

trend, _ = extract_trend_and_seasonality(ts, model=model)
if model not in [SeasonalityMode.ADDITIVE, ModelMode.ADDITIVE] and method == "STL":
logger.warn(
f"Only ADDITIVE seasonality is compatible with the STL method. Changing from {model} to"
f" {type(model).__name__}.ADDITIVE."
)
model = ModelMode.ADDITIVE
trend, _ = extract_trend_and_seasonality(ts, model=model, method=method, **kwargs)
new_ts = remove_from_series(ts, trend, model)
return new_ts

Expand Down