from sklearn.linear_model import LinearRegression
# We have 4 types of models
# - one-to-one: input a univariate series, output a univariate series
# - one-to-many: input a univariate series, output a multivariate series
# - many-to-one: input a multivariate series, output a univariate series
# - many-to-many: input a multivariate series, output a multivariate series
one2one_models = [
detector.ThresholdAD(),
detector.QuantileAD(),
detector.InterQuartileRangeAD(),
detector.GeneralizedESDTestAD(),
detector.PersistAD(),
detector.LevelShiftAD(),
detector.VolatilityShiftAD(),
detector.AutoregressionAD(),
detector.SeasonalAD(freq=2),
transformer.RollingAggregate(agg="median"),
transformer.RollingAggregate(agg="quantile", agg_params={"q": 0.5}),
transformer.DoubleRollingAggregate(agg="median"),
transformer.DoubleRollingAggregate(
agg="quantile", agg_params={"q": [0.1, 0.5, 0.9]}
),
transformer.DoubleRollingAggregate(
agg="hist", agg_params={"bins": [30, 50, 70]}
),
transformer.StandardScale(),
transformer.ClassicSeasonalDecomposition(freq=2),
]
)
# We have 4 types of models
# - one-to-one: input a univariate series, output a univariate series
# - one-to-many: input a univariate series, output a multivariate series
# - many-to-one: input a multivariate series, output a univariate series
# - many-to-many: input a multivariate series, output a multivariate series
one2one_models = [
detector.ThresholdAD(),
detector.QuantileAD(),
detector.InterQuartileRangeAD(),
detector.GeneralizedESDTestAD(),
detector.PersistAD(window=10),
detector.LevelShiftAD(window=10),
detector.VolatilityShiftAD(window=10),
detector.AutoregressionAD(),
detector.SeasonalAD(freq=2),
transformer.RollingAggregate(window=10, agg="median"),
transformer.RollingAggregate(
window=10, agg="quantile", agg_params={"q": 0.5}
),
transformer.DoubleRollingAggregate(window=10, agg="median"),
transformer.DoubleRollingAggregate(
window=10, agg="quantile", agg_params={"q": [0.1, 0.5, 0.9]}
),
transformer.DoubleRollingAggregate(
window=10, agg="hist", agg_params={"bins": [30, 50, 70]}
),
transformer.StandardScale(),
transformer.ClassicSeasonalDecomposition(freq=2),