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rename StatsForecast* models (#2762)
* rename StatsForecast* models * update changelog * update examples
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‎CHANGELOG.md‎

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@@ -12,7 +12,13 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
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**Improved**
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- Added support for categorical covariate to `CatBoostModel`. You can now define categorical components at model construction with parameters `categorical_*_covariates: List[str]` for past, future, and static covariates. [#2733](https://github.com/unit8co/darts/pull/2750) by [Jonas Blanc](https://github.com/jonasblanc).
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- Added new forecasting model: `StatsForecastAutoMFLES`, a simple time series method based on gradient boosting time series decomposition as proposed in [this repository](https://github.com/tblume1992/MFLES). This implementation is based on [AutoMFLES](https://nixtlaverse.nixtla.io/statsforecast/docs/models/mfles.html) from Nixtla's `statsforecasts` library. [#2747](https://github.com/unit8co/darts/pull/2747) by [Che Hang Ng](https://github.com/CheHangNg).
15+
- Added new forecasting model: `AutoMFLES`, a simple time series method based on gradient boosting time series decomposition as proposed in [this repository](https://github.com/tblume1992/MFLES). This implementation is based on [AutoMFLES](https://nixtlaverse.nixtla.io/statsforecast/docs/models/mfles.html) from Nixtla's `statsforecasts` library. [#2747](https://github.com/unit8co/darts/pull/2747) by [Che Hang Ng](https://github.com/CheHangNg).
16+
- 🔴 Simplified all `StatsForecast*` model names by removing the `StatsForecast` part. [#2762](https://github.com/unit8co/darts/pull/2762) by [Dennis Bader](https://github.com/dennisbader).
17+
- Renamed `StatsForecastAutoARIMA` to `AutoARIMA`
18+
- Renamed `StatsForecastAutoCES` to `AutoCES`
19+
- Renamed `StatsForecastAutoETS` to `AutoETS`
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- Renamed `StatsForecastAutoTBATS` to `AutoTBATS`
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- Renamed `StatsForecastAutoTheta` to `AutoTheta`
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**Removed / moved**
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‎README.md‎

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‎darts/models/__init__.py‎

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@@ -90,27 +90,27 @@
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try:
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from darts.models.forecasting.croston import Croston
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from darts.models.forecasting.sf_auto_arima import StatsForecastAutoARIMA
94-
from darts.models.forecasting.sf_auto_ces import StatsForecastAutoCES
95-
from darts.models.forecasting.sf_auto_ets import StatsForecastAutoETS
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from darts.models.forecasting.sf_auto_mfles import StatsForecastAutoMFLES
97-
from darts.models.forecasting.sf_auto_tbats import StatsForecastAutoTBATS
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from darts.models.forecasting.sf_auto_theta import StatsForecastAutoTheta
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from darts.models.forecasting.sf_auto_arima import AutoARIMA
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from darts.models.forecasting.sf_auto_ces import AutoCES
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from darts.models.forecasting.sf_auto_ets import AutoETS
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from darts.models.forecasting.sf_auto_mfles import AutoMFLES
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from darts.models.forecasting.sf_auto_tbats import AutoTBATS
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from darts.models.forecasting.sf_auto_theta import AutoTheta
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except ImportError:
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logger.warning(
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"The StatsForecast module could not be imported. "
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"To enable support for the StatsForecastAutoARIMA, "
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"StatsForecastAutoETS and Croston models, please consider "
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"To enable support for the AutoARIMA, "
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"AutoETS and Croston models, please consider "
105105
"installing it."
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)
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Croston = NotImportedModule(module_name="StatsForecast", warn=False)
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StatsForecastAutoARIMA = NotImportedModule(module_name="StatsForecast", warn=False)
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StatsForecastAutoCES = NotImportedModule(module_name="StatsForecast", warn=False)
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StatsForecastAutoETS = NotImportedModule(module_name="StatsForecast", warn=False)
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StatsForecastAutoMFLES = NotImportedModule(module_name="StatsForecast", warn=False)
112-
StatsForecastAutoTheta = NotImportedModule(module_name="StatsForecast", warn=False)
113-
StatsForecastAutoTBATS = NotImportedModule(module_name="StatsForecast", warn=False)
108+
AutoARIMA = NotImportedModule(module_name="StatsForecast", warn=False)
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AutoCES = NotImportedModule(module_name="StatsForecast", warn=False)
110+
AutoETS = NotImportedModule(module_name="StatsForecast", warn=False)
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AutoMFLES = NotImportedModule(module_name="StatsForecast", warn=False)
112+
AutoTheta = NotImportedModule(module_name="StatsForecast", warn=False)
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AutoTBATS = NotImportedModule(module_name="StatsForecast", warn=False)
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try:
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from darts.models.forecasting.xgboost import XGBModel
@@ -158,12 +158,12 @@
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"Prophet",
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"CatBoostModel",
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"Croston",
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"StatsForecastAutoARIMA",
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"StatsForecastAutoCES",
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"StatsForecastAutoETS",
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"StatsForecastAutoMFLES",
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"StatsForecastAutoTheta",
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"StatsForecastAutoTBATS",
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"AutoARIMA",
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"AutoCES",
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"AutoETS",
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"AutoMFLES",
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"AutoTheta",
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"AutoTBATS",
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"XGBModel",
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"GaussianProcessFilter",
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"KalmanFilter",

‎darts/models/forecasting/__init__.py‎

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@@ -14,17 +14,17 @@
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Statistical Models (`LocalForecastingModel <https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms>`_)
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- :class:`~darts.models.forecasting.arima.ARIMA`
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- :class:`~darts.models.forecasting.varima.VARIMA`
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- :class:`~darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA`
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- :class:`~darts.models.forecasting.sf_auto_arima.AutoARIMA`
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- :class:`~darts.models.forecasting.exponential_smoothing.ExponentialSmoothing`
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- :class:`~darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS`
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- :class:`~darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES`
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- :class:`~darts.models.forecasting.sf_auto_mfles.StatsForecastAutoMFLES`
19+
- :class:`~darts.models.forecasting.sf_auto_ets.AutoETS`
20+
- :class:`~darts.models.forecasting.sf_auto_ces.AutoCES`
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- :class:`~darts.models.forecasting.sf_auto_mfles.AutoMFLES`
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- :class:`~darts.models.forecasting.tbats_model.BATS`
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- :class:`~darts.models.forecasting.tbats_model.TBATS`
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- :class:`~darts.models.forecasting.sf_auto_tbats.StatsForecastAutoTBATS`
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- :class:`~darts.models.forecasting.sf_auto_tbats.AutoTBATS`
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- :class:`~darts.models.forecasting.theta.Theta`
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- :class:`~darts.models.forecasting.theta.FourTheta`
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- :class:`~darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta`
27+
- :class:`~darts.models.forecasting.sf_auto_theta.AutoTheta`
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- :class:`~darts.models.forecasting.prophet_model.Prophet`
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- :class:`~Fast Fourier Transform) <darts.models.forecasting.fft.FFT`
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- :class:`~darts.models.forecasting.kalman_forecaster.KalmanForecaster`

‎darts/models/forecasting/sf_auto_arima.py‎

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@@ -1,5 +1,5 @@
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"""
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StatsForecastAutoARIMA
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AutoARIMA
33
-----------
44
"""
55

@@ -18,7 +18,7 @@
1818
)
1919

2020

21-
class StatsForecastAutoARIMA(FutureCovariatesLocalForecastingModel):
21+
class AutoARIMA(FutureCovariatesLocalForecastingModel):
2222
def __init__(
2323
self, *autoarima_args, add_encoders: Optional[dict] = None, **autoarima_kwargs
2424
):
@@ -69,13 +69,13 @@ def encode_year(idx):
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Examples
7070
--------
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>>> from darts.datasets import AirPassengersDataset
72-
>>> from darts.models import StatsForecastAutoARIMA
72+
>>> from darts.models import AutoARIMA
7373
>>> from darts.utils.timeseries_generation import datetime_attribute_timeseries
7474
>>> series = AirPassengersDataset().load()
7575
>>> # optionally, use some future covariates; e.g. the value of the month encoded as a sine and cosine series
7676
>>> future_cov = datetime_attribute_timeseries(series, "month", cyclic=True, add_length=6)
77-
>>> # define StatsForecastAutoARIMA parameters
78-
>>> model = StatsForecastAutoARIMA(season_length=12)
77+
>>> # define AutoARIMA parameters
78+
>>> model = AutoARIMA(season_length=12)
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>>> model.fit(series, future_covariates=future_cov)
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>>> pred = model.predict(6, future_covariates=future_cov)
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>>> pred.values()

‎darts/models/forecasting/sf_auto_ces.py‎

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"""
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StatsForecastAutoCES
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AutoCES
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-----------
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"""
55

@@ -9,7 +9,7 @@
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from darts.models.forecasting.forecasting_model import LocalForecastingModel
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1111

12-
class StatsForecastAutoCES(LocalForecastingModel):
12+
class AutoCES(LocalForecastingModel):
1313
def __init__(self, *autoces_args, **autoces_kwargs):
1414
"""Auto-CES based on `Statsforecasts package
1515
<https://github.com/Nixtla/statsforecast>`_.
@@ -31,10 +31,10 @@ def __init__(self, *autoces_args, **autoces_kwargs):
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Examples
3232
--------
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>>> from darts.datasets import AirPassengersDataset
34-
>>> from darts.models import StatsForecastAutoCES
34+
>>> from darts.models import AutoCES
3535
>>> series = AirPassengersDataset().load()
36-
>>> # define StatsForecastAutoCES parameters
37-
>>> model = StatsForecastAutoCES(season_length=12, model="Z")
36+
>>> # define AutoCES parameters
37+
>>> model = AutoCES(season_length=12, model="Z")
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>>> model.fit(series)
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>>> pred = model.predict(6)
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>>> pred.values()

‎darts/models/forecasting/sf_auto_ets.py‎

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"""
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StatsForecastAutoETS
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AutoETS
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-----------
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"""
55

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)
2020

2121

22-
class StatsForecastAutoETS(FutureCovariatesLocalForecastingModel):
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class AutoETS(FutureCovariatesLocalForecastingModel):
2323
def __init__(
2424
self, *autoets_args, add_encoders: Optional[dict] = None, **autoets_kwargs
2525
):
@@ -74,13 +74,13 @@ def encode_year(idx):
7474
Examples
7575
--------
7676
>>> from darts.datasets import AirPassengersDataset
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>>> from darts.models import StatsForecastAutoETS
77+
>>> from darts.models import AutoETS
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>>> from darts.utils.timeseries_generation import datetime_attribute_timeseries
7979
>>> series = AirPassengersDataset().load()
8080
>>> # optionally, use some future covariates; e.g. the value of the month encoded as a sine and cosine series
8181
>>> future_cov = datetime_attribute_timeseries(series, "month", cyclic=True, add_length=6)
82-
>>> # define StatsForecastAutoETS parameters
83-
>>> model = StatsForecastAutoETS(season_length=12, model="AZZ")
82+
>>> # define AutoETS parameters
83+
>>> model = AutoETS(season_length=12, model="AZZ")
8484
>>> model.fit(series, future_covariates=future_cov)
8585
>>> pred = model.predict(6, future_covariates=future_cov)
8686
>>> pred.values()

‎darts/models/forecasting/sf_auto_mfles.py‎

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"""
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StatsForecastAutoMFLES
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AutoMFLES
33
-----------
44
"""
55

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1616
logger = get_logger(__name__)
1717

1818

19-
class StatsForecastAutoMFLES(FutureCovariatesLocalForecastingModel):
19+
class AutoMFLES(FutureCovariatesLocalForecastingModel):
2020
def __init__(
2121
self, *autoMFLES_args, add_encoders: Optional[dict] = None, **autoMFLES_kwargs
2222
):
@@ -64,13 +64,13 @@ def encode_year(idx):
6464
Examples
6565
--------
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>>> from darts.datasets import AirPassengersDataset
67-
>>> from darts.models import StatsForecastAutoMFLES
67+
>>> from darts.models import AutoMFLES
6868
>>> from darts.utils.timeseries_generation import datetime_attribute_timeseries
6969
>>> series = AirPassengersDataset().load()
7070
>>> # optionally, use some future covariates; e.g. the value of the month encoded as a sine and cosine series
7171
>>> future_cov = datetime_attribute_timeseries(series, "month", cyclic=True, add_length=6)
72-
>>> # define StatsForecastAutoMFLES parameters
73-
>>> model = StatsForecastAutoMFLES(season_length=12, test_size=12)
72+
>>> # define AutoMFLES parameters
73+
>>> model = AutoMFLES(season_length=12, test_size=12)
7474
>>> model.fit(series, future_covariates=future_cov)
7575
>>> pred = model.predict(6, future_covariates=future_cov)
7676
>>> pred.values()
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8383
"""
8484
if "prediction_intervals" in autoMFLES_kwargs:
8585
logger.warning(
86-
"StatsForecastAutoMFLES does not support probabilistic forecasting. "
86+
"AutoMFLES does not support probabilistic forecasting. "
8787
"`prediction_intervals` will be ignored."
8888
)
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‎darts/models/forecasting/sf_auto_tbats.py‎

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"""
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StatsForecastAutoTBATS
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AutoTBATS
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-----------
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"""
55

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1414
from darts.models.forecasting.forecasting_model import LocalForecastingModel
1515

1616

17-
class StatsForecastAutoTBATS(LocalForecastingModel):
17+
class AutoTBATS(LocalForecastingModel):
1818
def __init__(self, *autoTBATS_args, **autoTBATS_kwargs):
1919
"""Auto-TBATS based on `Statsforecasts package
2020
<https://github.com/Nixtla/statsforecast>`_.
@@ -40,10 +40,10 @@ def __init__(self, *autoTBATS_args, **autoTBATS_kwargs):
4040
Examples
4141
--------
4242
>>> from darts.datasets import AirPassengersDataset
43-
>>> from darts.models import StatsForecastAutoTBATS
43+
>>> from darts.models import AutoTBATS
4444
>>> series = AirPassengersDataset().load()
45-
>>> # define StatsForecastAutoTBATS parameters
46-
>>> model = StatsForecastAutoTBATS(season_length=12)
45+
>>> # define AutoTBATS parameters
46+
>>> model = AutoTBATS(season_length=12)
4747
>>> model.fit(series)
4848
>>> pred = model.predict(6)
4949
>>> pred.values()

‎darts/models/forecasting/sf_auto_theta.py‎

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"""
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StatsForecastAutoTheta
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AutoTheta
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-----------
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"""
55

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1414
from darts.models.forecasting.forecasting_model import LocalForecastingModel
1515

1616

17-
class StatsForecastAutoTheta(LocalForecastingModel):
17+
class AutoTheta(LocalForecastingModel):
1818
def __init__(self, *autotheta_args, **autotheta_kwargs):
1919
"""Auto-Theta based on `Statsforecasts package
2020
<https://github.com/Nixtla/statsforecast>`_.
@@ -39,10 +39,10 @@ def __init__(self, *autotheta_args, **autotheta_kwargs):
3939
Examples
4040
--------
4141
>>> from darts.datasets import AirPassengersDataset
42-
>>> from darts.models import StatsForecastAutoTheta
42+
>>> from darts.models import AutoTheta
4343
>>> series = AirPassengersDataset().load()
44-
>>> # define StatsForecastAutoTheta parameters
45-
>>> model = StatsForecastAutoTheta(season_length=12)
44+
>>> # define AutoTheta parameters
45+
>>> model = AutoTheta(season_length=12)
4646
>>> model.fit(series)
4747
>>> pred = model.predict(6)
4848
>>> pred.values()

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