Index

_

__init__() (pytorch_forecasting.data.data_module._encoder_decoder_data_module.EncoderDecoderTimeSeriesDataModule method) (pytorch_forecasting.data.data_module._tslib_data_module.TslibDataModule method) (pytorch_forecasting.data.encoders.EncoderNormalizer method) (pytorch_forecasting.data.encoders.GroupNormalizer method) (pytorch_forecasting.data.encoders.MultiNormalizer method) (pytorch_forecasting.data.encoders.NaNLabelEncoder method) (pytorch_forecasting.data.encoders.TorchNormalizer method) (pytorch_forecasting.data.samplers.GroupedSampler method) (pytorch_forecasting.data.samplers.TimeSynchronizedBatchSampler method) (pytorch_forecasting.data.timeseries._timeseries_v2.TimeSeries method) (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) (pytorch_forecasting.metrics.distributions.BetaDistributionLoss method) (pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss method) (pytorch_forecasting.metrics.distributions.LogNormalDistributionLoss method) (pytorch_forecasting.metrics.distributions.MQF2DistributionLoss method) (pytorch_forecasting.metrics.distributions.MultivariateNormalDistributionLoss method) (pytorch_forecasting.metrics.distributions.NegativeBinomialDistributionLoss method) (pytorch_forecasting.metrics.distributions.NormalDistributionLoss method) (pytorch_forecasting.metrics.point.CrossEntropy method) (pytorch_forecasting.metrics.point.MAE method) (pytorch_forecasting.metrics.point.MAPE method) (pytorch_forecasting.metrics.point.MASE method) (pytorch_forecasting.metrics.point.PoissonLoss method) (pytorch_forecasting.metrics.point.RMSE method) (pytorch_forecasting.metrics.point.SMAPE method) (pytorch_forecasting.metrics.point.TweedieLoss method) (pytorch_forecasting.metrics.quantile.QuantileLoss method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method) (pytorch_forecasting.models.base._tslib_base_model_v2.TslibBaseModel method) (pytorch_forecasting.models.deepar._deepar_pkg.DeepAR_pkg method) (pytorch_forecasting.models.deepar.DeepAR method) (pytorch_forecasting.models.dlinear._dlinear_pkg_v2.DLinear_pkg_v2 method) (pytorch_forecasting.models.dlinear._dlinear_v2.DLinear method) (pytorch_forecasting.models.mlp._decodermlp_pkg.DecoderMLP_pkg method) (pytorch_forecasting.models.mlp.DecoderMLP method) (pytorch_forecasting.models.nbeats._nbeats_pkg.NBeats_pkg method) (pytorch_forecasting.models.nbeats._nbeatskan_pkg.NBeatsKAN_pkg method) (pytorch_forecasting.models.nbeats.NBeats method) (pytorch_forecasting.models.nbeats.NBeatsKAN method) (pytorch_forecasting.models.nhits._nhits_pkg.NHiTS_pkg method) (pytorch_forecasting.models.nhits.NHiTS method) (pytorch_forecasting.models.rnn._rnn_pkg.RecurrentNetwork_pkg method) (pytorch_forecasting.models.rnn.RecurrentNetwork method) (pytorch_forecasting.models.samformer._samformer_v2.Samformer method) (pytorch_forecasting.models.samformer._samformer_v2_pkg.Samformer_pkg_v2 method) (pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg.TemporalFusionTransformer_pkg method) (pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg_v2.TFT_pkg_v2 method) (pytorch_forecasting.models.temporal_fusion_transformer._tft_v2.TFT method) (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method) (pytorch_forecasting.models.tide._tide_dsipts._tide_v2.TIDE method) (pytorch_forecasting.models.tide._tide_dsipts._tide_v2_pkg.TIDE_pkg_v2 method) (pytorch_forecasting.models.tide._tide_pkg.TiDEModel_pkg method) (pytorch_forecasting.models.tide.TiDEModel method) (pytorch_forecasting.models.timexer._timexer_pkg_v2.TimeXer_pkg_v2 method) (pytorch_forecasting.models.timexer._timexer_v2.TimeXer method) (pytorch_forecasting.models.timexer.TimeXer method) (pytorch_forecasting.models.xlstm.xLSTMTime method)

_check_matplotlib() (in module pytorch_forecasting.utils._dependencies._dependencies) _coerce_to_dict() (in module pytorch_forecasting.utils._coerce) _coerce_to_list() (in module pytorch_forecasting.utils._coerce) _get_deps_info() (in module pytorch_forecasting.utils._maint._show_versions) _get_sys_info() (in module pytorch_forecasting.utils._maint._show_versions) _get_test_names_for_obj() (in module pytorch_forecasting.utils._estimator_checks) _get_test_names_from_class() (in module pytorch_forecasting.utils._estimator_checks)

A

allow_zero_length_dataloader_with_multiple_devices (pytorch_forecasting.data.data_module._encoder_decoder_data_module.EncoderDecoderTimeSeriesDataModule attribute), [1] (pytorch_forecasting.data.data_module._tslib_data_module.TslibDataModule attribute), [1]

apply_to_list() (in module pytorch_forecasting.utils._utils) autocorrelation() (in module pytorch_forecasting.utils._utils)

B

BaseModel (class in pytorch_forecasting.models.base._base_model_v2)

BetaDistributionLoss (class in pytorch_forecasting.metrics.distributions)

C

check_estimator() (in module pytorch_forecasting.utils._estimator_checks) classproperty (class in pytorch_forecasting.utils._classproperty)

concat_sequences() (in module pytorch_forecasting.utils._utils) create_mask() (in module pytorch_forecasting.utils._utils) CrossEntropy (class in pytorch_forecasting.metrics.point)

D

DecoderMLP (class in pytorch_forecasting.models.mlp) DecoderMLP_pkg (class in pytorch_forecasting.models.mlp._decodermlp_pkg) DeepAR (class in pytorch_forecasting.models.deepar)

DeepAR_pkg (class in pytorch_forecasting.models.deepar._deepar_pkg) detach() (in module pytorch_forecasting.utils._utils) DLinear (class in pytorch_forecasting.models.dlinear._dlinear_v2) DLinear_pkg_v2 (class in pytorch_forecasting.models.dlinear._dlinear_pkg_v2)

E

EncoderDecoderTimeSeriesDataModule (class in pytorch_forecasting.data.data_module._encoder_decoder_data_module)

EncoderNormalizer (class in pytorch_forecasting.data.encoders) extra_repr() (pytorch_forecasting.utils._utils.InitialParameterRepresenterMixIn method)

G

get_embedding_size() (in module pytorch_forecasting.utils._utils) groupby_apply() (in module pytorch_forecasting.utils._utils)

GroupedSampler (class in pytorch_forecasting.data.samplers) GroupNormalizer (class in pytorch_forecasting.data.encoders)

I

iget() (pytorch_forecasting.utils._utils.OutputMixIn method) ImplicitQuantileNetworkDistributionLoss (class in pytorch_forecasting.metrics.distributions)

InitialParameterRepresenterMixIn (class in pytorch_forecasting.utils._utils) integer_histogram() (in module pytorch_forecasting.utils._utils)

L

LogNormalDistributionLoss (class in pytorch_forecasting.metrics.distributions)

M

MAE (class in pytorch_forecasting.metrics.point) MAPE (class in pytorch_forecasting.metrics.point) MASE (class in pytorch_forecasting.metrics.point) masked_op() (in module pytorch_forecasting.utils._utils) module pytorch_forecasting.utils pytorch_forecasting.utils._classproperty pytorch_forecasting.utils._coerce pytorch_forecasting.utils._dependencies pytorch_forecasting.utils._dependencies._dependencies pytorch_forecasting.utils._dependencies.tests pytorch_forecasting.utils._dependencies.tests.test_safe_import pytorch_forecasting.utils._estimator_checks pytorch_forecasting.utils._maint pytorch_forecasting.utils._maint._show_versions pytorch_forecasting.utils._utils

move_to_device() (in module pytorch_forecasting.utils._utils) MQF2DistributionLoss (class in pytorch_forecasting.metrics.distributions) MultiNormalizer (class in pytorch_forecasting.data.encoders) MultivariateNormalDistributionLoss (class in pytorch_forecasting.metrics.distributions)

N

NaNLabelEncoder (class in pytorch_forecasting.data.encoders) NBeats (class in pytorch_forecasting.models.nbeats) NBeats_pkg (class in pytorch_forecasting.models.nbeats._nbeats_pkg) NBeatsKAN (class in pytorch_forecasting.models.nbeats) NBeatsKAN_pkg (class in pytorch_forecasting.models.nbeats._nbeatskan_pkg)

NegativeBinomialDistributionLoss (class in pytorch_forecasting.metrics.distributions) next_fast_len() (in module pytorch_forecasting.utils._utils) NHiTS (class in pytorch_forecasting.models.nhits) NHiTS_pkg (class in pytorch_forecasting.models.nhits._nhits_pkg) NormalDistributionLoss (class in pytorch_forecasting.metrics.distributions)

O

OutputMixIn (class in pytorch_forecasting.utils._utils)

P

padded_stack() (in module pytorch_forecasting.utils._utils) parametrize_with_checks() (in module pytorch_forecasting.utils._estimator_checks) PoissonLoss (class in pytorch_forecasting.metrics.point) prepare_data_per_node (pytorch_forecasting.data.data_module._encoder_decoder_data_module.EncoderDecoderTimeSeriesDataModule attribute), [1] (pytorch_forecasting.data.data_module._tslib_data_module.TslibDataModule attribute), [1] profile() (in module pytorch_forecasting.utils._utils) pytorch_forecasting.utils module pytorch_forecasting.utils._classproperty module pytorch_forecasting.utils._coerce module pytorch_forecasting.utils._dependencies module

pytorch_forecasting.utils._dependencies._dependencies module pytorch_forecasting.utils._dependencies.tests module pytorch_forecasting.utils._dependencies.tests.test_safe_import module pytorch_forecasting.utils._estimator_checks module pytorch_forecasting.utils._maint module pytorch_forecasting.utils._maint._show_versions module pytorch_forecasting.utils._utils module

Q

QuantileLoss (class in pytorch_forecasting.metrics.quantile)

R

RecurrentNetwork (class in pytorch_forecasting.models.rnn) RecurrentNetwork_pkg (class in pytorch_forecasting.models.rnn._rnn_pkg)

repr_class() (in module pytorch_forecasting.utils._utils) RMSE (class in pytorch_forecasting.metrics.point)

S

Samformer (class in pytorch_forecasting.models.samformer._samformer_v2) Samformer_pkg_v2 (class in pytorch_forecasting.models.samformer._samformer_v2_pkg)

show_versions() (in module pytorch_forecasting.utils._maint._show_versions) SMAPE (class in pytorch_forecasting.metrics.point)

T

TemporalFusionTransformer (class in pytorch_forecasting.models.temporal_fusion_transformer) TemporalFusionTransformer_pkg (class in pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg) test_import_class() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) test_import_existing_object() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) test_import_missing_module() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) test_import_submodule() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) test_import_with_different_pkg_name_1() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) test_import_with_different_pkg_name_2() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) test_import_without_pkg_name() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) test_multiple_inheritance_from_mock() (in module pytorch_forecasting.utils._dependencies.tests.test_safe_import) TFT (class in pytorch_forecasting.models.temporal_fusion_transformer._tft_v2) TFT_pkg_v2 (class in pytorch_forecasting.models.temporal_fusion_transformer._tft_pkg_v2) TIDE (class in pytorch_forecasting.models.tide._tide_dsipts._tide_v2) TIDE_pkg_v2 (class in pytorch_forecasting.models.tide._tide_dsipts._tide_v2_pkg)

TiDEModel (class in pytorch_forecasting.models.tide) TiDEModel_pkg (class in pytorch_forecasting.models.tide._tide_pkg) TimeSeries (class in pytorch_forecasting.data.timeseries._timeseries_v2) TimeSeriesDataSet (class in pytorch_forecasting.data.timeseries) TimeSynchronizedBatchSampler (class in pytorch_forecasting.data.samplers) TimeXer (class in pytorch_forecasting.models.timexer) (class in pytorch_forecasting.models.timexer._timexer_v2) TimeXer_pkg_v2 (class in pytorch_forecasting.models.timexer._timexer_pkg_v2) to_list() (in module pytorch_forecasting.utils._utils) to_network_output() (pytorch_forecasting.utils._utils.TupleOutputMixIn method) TorchNormalizer (class in pytorch_forecasting.data.encoders) TslibBaseModel (class in pytorch_forecasting.models.base._tslib_base_model_v2) TslibDataModule (class in pytorch_forecasting.data.data_module._tslib_data_module) TupleOutputMixIn (class in pytorch_forecasting.utils._utils) TweedieLoss (class in pytorch_forecasting.metrics.point)

U

unpack_sequence() (in module pytorch_forecasting.utils._utils)

unsqueeze_like() (in module pytorch_forecasting.utils._utils)

X

xLSTMTime (class in pytorch_forecasting.models.xlstm)