Index

_

_BaseObject (class in pytorch_forecasting.models.base._base_object) _BasePtForecaster (class in pytorch_forecasting.models.base._base_object) _check_matplotlib() (in module pytorch_forecasting.utils._dependencies._dependencies) _clipped_log() (in module pytorch_forecasting.data.encoders) _clipped_logit() (in module pytorch_forecasting.data.encoders) _coerce_to_dict() (in module pytorch_forecasting.utils._coerce) _coerce_to_list() (in module pytorch_forecasting.utils._coerce) _concatenate_output() (in module pytorch_forecasting.models.base._base_model) _find_end_indices() (in module pytorch_forecasting.data.timeseries._timeseries) _get_data_by_filename() (in module pytorch_forecasting.data.examples)

_get_deps_info() (in module pytorch_forecasting.utils._maint._show_versions) _get_installed_packages() (in module pytorch_forecasting.utils._dependencies._dependencies) _get_installed_packages_private() (in module pytorch_forecasting.utils._dependencies._dependencies) _get_sys_info() (in module pytorch_forecasting.utils._maint._show_versions) _identity() (in module pytorch_forecasting.data.encoders) _minus_one() (in module pytorch_forecasting.data.encoders) _plus_one() (in module pytorch_forecasting.data.encoders) _ResidualBlock (class in pytorch_forecasting.models.tide.sub_modules) _square() (in module pytorch_forecasting.data.encoders) _TideModule (class in pytorch_forecasting.models.tide.sub_modules) _torch_cat_na() (in module pytorch_forecasting.models.base._base_model)

A

AddNorm (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) AggregationMetric (class in pytorch_forecasting.metrics.base_metrics) allow_zero_length_dataloader_with_multiple_devices (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule attribute) apply_to_list() (in module pytorch_forecasting.utils._utils)

AttentionLayer (class in pytorch_forecasting.models.timexer.sub_modules) autocorrelation() (in module pytorch_forecasting.utils._utils) AutoRegressiveBaseModel (class in pytorch_forecasting.models.base._base_model) AutoRegressiveBaseModelWithCovariates (class in pytorch_forecasting.models.base._base_model)

B

Baseline (class in pytorch_forecasting.models.baseline) BaseModel (class in pytorch_forecasting.models.base._base_model) (class in pytorch_forecasting.models.base._base_model_v2)

BaseModelWithCovariates (class in pytorch_forecasting.models.base._base_model) batch_shape (pytorch_forecasting.metrics._mqf2_utils.MQF2Distribution property) BetaDistributionLoss (class in pytorch_forecasting.metrics.distributions)

C

calculate_decoder_length() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) calculate_prediction_actual_by_variable() (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates method) categorical_groups_mapping (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates property) categoricals (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates property) check_for_nonfinite() (in module pytorch_forecasting.data.timeseries._timeseries) classproperty (class in pytorch_forecasting.utils._classproperty) CompositeMetric (class in pytorch_forecasting.metrics.base_metrics) compute() (pytorch_forecasting.metrics.base_metrics.AggregationMetric method) (pytorch_forecasting.metrics.base_metrics.CompositeMetric method) (pytorch_forecasting.metrics.base_metrics.Metric method) (pytorch_forecasting.metrics.base_metrics.MultiHorizonMetric method) (pytorch_forecasting.metrics.base_metrics.MultiLoss method) (pytorch_forecasting.metrics.base_metrics.TorchMetricWrapper method)

concat_sequences() (in module pytorch_forecasting.utils._utils) configure_optimizers() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method) construct_batch_groups() (pytorch_forecasting.data.samplers.GroupedSampler method) construct_input_vector() (pytorch_forecasting.models.deepar._deepar.DeepAR method) (pytorch_forecasting.models.rnn._rnn.RecurrentNetwork method) convert_torchmetric_to_pytorch_forecasting_metric() (in module pytorch_forecasting.metrics.base_metrics) create_log() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.deepar._deepar.DeepAR method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) create_mask() (in module pytorch_forecasting.utils._utils) create_test_instance() (pytorch_forecasting.models.base._base_object._BasePtForecaster class method) create_test_instances_and_names() (pytorch_forecasting.models.base._base_object._BasePtForecaster class method) CrossEntropy (class in pytorch_forecasting.metrics.point) current_stage (pytorch_forecasting.models.base._base_model.BaseModel property)

D

DataEmbedding_inverted (class in pytorch_forecasting.models.timexer.sub_modules) decode() (pytorch_forecasting.models.deepar._deepar.DeepAR method) (pytorch_forecasting.models.rnn._rnn.RecurrentNetwork method) decode_autoregressive() (pytorch_forecasting.models.base._base_model.AutoRegressiveBaseModel method) decoded_index (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) decoder_covariate_size (pytorch_forecasting.models.nhits._nhits.NHiTS property) (pytorch_forecasting.models.tide._tide.TiDEModel property) (pytorch_forecasting.models.timexer._timexer.TimeXer property) decoder_variables (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates property) DecoderMLP (class in pytorch_forecasting.models.mlp._decodermlp) DecoderMLP_pkg (class in pytorch_forecasting.models.mlp._decodermlp_pkg) deduce_default_output_parameters() (pytorch_forecasting.models.base._base_model.BaseModel static method)

DeepAR (class in pytorch_forecasting.models.deepar._deepar) DeepAR_pkg (class in pytorch_forecasting.models.deepar._deepar_pkg) DeepConvexNet (class in pytorch_forecasting.metrics._mqf2_utils) detach() (in module pytorch_forecasting.utils._utils) distribution_arguments (pytorch_forecasting.metrics.base_metrics.DistributionLoss attribute) distribution_class (pytorch_forecasting.metrics.base_metrics.DistributionLoss attribute) (pytorch_forecasting.metrics.distributions.BetaDistributionLoss attribute) (pytorch_forecasting.metrics.distributions.LogNormalDistributionLoss attribute) (pytorch_forecasting.metrics.distributions.MultivariateNormalDistributionLoss attribute) (pytorch_forecasting.metrics.distributions.NegativeBinomialDistributionLoss attribute) (pytorch_forecasting.metrics.distributions.NormalDistributionLoss attribute) DistributionLoss (class in pytorch_forecasting.metrics.base_metrics) dropout_categoricals (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property)

E

encode() (pytorch_forecasting.models.deepar._deepar.DeepAR method) (pytorch_forecasting.models.rnn._rnn.RecurrentNetwork method) Encoder (class in pytorch_forecasting.models.timexer.sub_modules) encoder_covariate_size (pytorch_forecasting.models.nhits._nhits.NHiTS property) (pytorch_forecasting.models.tide._tide.TiDEModel property) (pytorch_forecasting.models.timexer._timexer.TimeXer property) encoder_variables (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates property) EncoderDecoderTimeSeriesDataModule (class in pytorch_forecasting.data.data_module) EncoderLayer (class in pytorch_forecasting.models.timexer.sub_modules)

EncoderNormalizer (class in pytorch_forecasting.data.encoders) EnEmbedding (class in pytorch_forecasting.models.timexer.sub_modules) energy_score() (pytorch_forecasting.metrics._mqf2_utils.MQF2Distribution method) (pytorch_forecasting.metrics._mqf2_utils.SequentialNet method) es_sample() (pytorch_forecasting.metrics._mqf2_utils.SequentialNet method) event_shape (pytorch_forecasting.metrics._mqf2_utils.MQF2Distribution property) expand_static_context() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) Expm1Transform (class in pytorch_forecasting.data.encoders) extra_repr() (pytorch_forecasting.metrics.base_metrics.Metric method) extract_features() (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates method)

F

filter() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) fit() (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) fit_transform() (pytorch_forecasting.data.encoders.GroupNormalizer method) (pytorch_forecasting.data.encoders.NaNLabelEncoder method) flat_categoricals (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) FlattenHead (class in pytorch_forecasting.models.timexer.sub_modules) forward() (pytorch_forecasting.metrics._mqf2_utils.SequentialNet method) (pytorch_forecasting.metrics.base_metrics.AggregationMetric method) (pytorch_forecasting.metrics.base_metrics.CompositeMetric method) (pytorch_forecasting.metrics.base_metrics.MultiLoss method) (pytorch_forecasting.metrics.base_metrics.TorchMetricWrapper method) (pytorch_forecasting.metrics.distributions.ImplicitQuantileNetwork method) (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method) (pytorch_forecasting.models.baseline.Baseline method) (pytorch_forecasting.models.deepar._deepar.DeepAR method) (pytorch_forecasting.models.mlp._decodermlp.DecoderMLP method) (pytorch_forecasting.models.mlp.submodules.FullyConnectedModule method) (pytorch_forecasting.models.nbeats._nbeats.NBeats method) (pytorch_forecasting.models.nbeats.sub_modules.NBEATSBlock method) (pytorch_forecasting.models.nbeats.sub_modules.NBEATSGenericBlock method) (pytorch_forecasting.models.nbeats.sub_modules.NBEATSSeasonalBlock method) (pytorch_forecasting.models.nbeats.sub_modules.NBEATSTrendBlock method) (pytorch_forecasting.models.nhits._nhits.NHiTS method) (pytorch_forecasting.models.nhits.sub_modules.IdentityBasis method) (pytorch_forecasting.models.nhits.sub_modules.MLP method) (pytorch_forecasting.models.nhits.sub_modules.NHiTS method) (pytorch_forecasting.models.nhits.sub_modules.NHiTSBlock method) (pytorch_forecasting.models.nhits.sub_modules.StaticFeaturesEncoder method) (pytorch_forecasting.models.nn.embeddings.MultiEmbedding method) (pytorch_forecasting.models.nn.embeddings.TimeDistributedEmbeddingBag method) (pytorch_forecasting.models.nn.rnn.RNN method) (pytorch_forecasting.models.rnn._rnn.RecurrentNetwork method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) (pytorch_forecasting.models.temporal_fusion_transformer._tft_v2.TFT method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.AddNorm method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.GateAddNorm method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.GatedLinearUnit method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.GatedResidualNetwork method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.InterpretableMultiHeadAttention method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.PositionalEncoder method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.ResampleNorm method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.ScaledDotProductAttention method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.TimeDistributed method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.TimeDistributedInterpolation method) (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules.VariableSelectionNetwork method) (pytorch_forecasting.models.tide._tide.TiDEModel method) (pytorch_forecasting.models.tide.sub_modules._ResidualBlock method) (pytorch_forecasting.models.tide.sub_modules._TideModule method) (pytorch_forecasting.models.timexer._timexer.TimeXer method) (pytorch_forecasting.models.timexer.sub_modules.AttentionLayer method) (pytorch_forecasting.models.timexer.sub_modules.DataEmbedding_inverted method) (pytorch_forecasting.models.timexer.sub_modules.Encoder method) (pytorch_forecasting.models.timexer.sub_modules.EncoderLayer method) (pytorch_forecasting.models.timexer.sub_modules.EnEmbedding method) (pytorch_forecasting.models.timexer.sub_modules.FlattenHead method) (pytorch_forecasting.models.timexer.sub_modules.FullAttention method) (pytorch_forecasting.models.timexer.sub_modules.PositionalEmbedding method)

forward_transform() (pytorch_forecasting.metrics._mqf2_utils.DeepConvexNet method) from_dataset() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet class method) (pytorch_forecasting.models.base._base_model.AutoRegressiveBaseModel class method) (pytorch_forecasting.models.base._base_model.BaseModel class method) (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates class method) (pytorch_forecasting.models.deepar._deepar.DeepAR class method) (pytorch_forecasting.models.mlp._decodermlp.DecoderMLP class method) (pytorch_forecasting.models.nbeats._nbeats.NBeats class method) (pytorch_forecasting.models.nhits._nhits.NHiTS class method) (pytorch_forecasting.models.rnn._rnn.RecurrentNetwork class method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer class method) (pytorch_forecasting.models.tide._tide.TiDEModel class method) (pytorch_forecasting.models.timexer._timexer.TimeXer class method) from_parameters() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet class method) FullAttention (class in pytorch_forecasting.models.timexer.sub_modules) FullyConnectedModule (class in pytorch_forecasting.models.mlp.submodules)

G

GateAddNorm (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) GatedLinearUnit (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) GatedResidualNetwork (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) generate_ar_data() (in module pytorch_forecasting.data.examples) get_attention_mask() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) get_embedding_size() (in module pytorch_forecasting.utils._utils) get_groups() (pytorch_forecasting.data.samplers.GroupedSampler method) (pytorch_forecasting.data.samplers.TimeSynchronizedBatchSampler method) get_metadata() (pytorch_forecasting.data.timeseries._timeseries_v2.TimeSeries method) get_model_cls() (pytorch_forecasting.models.base._base_object._BasePtForecaster class method) (pytorch_forecasting.models.deepar._deepar_pkg.DeepAR_pkg class method) (pytorch_forecasting.models.mlp._decodermlp_pkg.DecoderMLP_pkg class method) (pytorch_forecasting.models.nbeats._nbeats_pkg.NBeats_pkg class method) (pytorch_forecasting.models.tide._tide_pkg.TiDEModel_pkg class method) (pytorch_forecasting.models.timexer._timexer_pkg.TimeXer_pkg class method) get_norm() (pytorch_forecasting.data.encoders.GroupNormalizer method) get_parameters() (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.timeseries._timeseries.TimeSeriesDataSet method)

get_potential() (pytorch_forecasting.metrics._mqf2_utils.DeepConvexNet method) get_rnn() (in module pytorch_forecasting.models.nn.rnn) get_stallion_data() (in module pytorch_forecasting.data.examples) get_test_train_params() (pytorch_forecasting.models.deepar._deepar_pkg.DeepAR_pkg class method) (pytorch_forecasting.models.mlp._decodermlp_pkg.DecoderMLP_pkg class method) (pytorch_forecasting.models.nbeats._nbeats_pkg.NBeats_pkg class method) (pytorch_forecasting.models.tide._tide_pkg.TiDEModel_pkg class method) (pytorch_forecasting.models.timexer._timexer_pkg.TimeXer_pkg class method) get_transform() (pytorch_forecasting.data.encoders.TransformMixIn class method) get_transformer() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) groupby_apply() (in module pytorch_forecasting.utils._utils) GroupedSampler (class in pytorch_forecasting.data.samplers) GroupNormalizer (class in pytorch_forecasting.data.encoders) GRU (class in pytorch_forecasting.models.nn.rnn)

H

handle_no_encoding() (pytorch_forecasting.models.nn.rnn.GRU method) (pytorch_forecasting.models.nn.rnn.LSTM method) (pytorch_forecasting.models.nn.rnn.RNN method)

I

IdentityBasis (class in pytorch_forecasting.models.nhits.sub_modules) iget() (pytorch_forecasting.utils._utils.OutputMixIn method) ImplicitQuantileNetwork (class in pytorch_forecasting.metrics.distributions) ImplicitQuantileNetworkDistributionLoss (class in pytorch_forecasting.metrics.distributions) init_hidden_state() (pytorch_forecasting.models.nn.rnn.GRU method) (pytorch_forecasting.models.nn.rnn.LSTM method) (pytorch_forecasting.models.nn.rnn.RNN method) init_weights() (in module pytorch_forecasting.models.nhits.sub_modules)

InitialParameterRepresenterMixIn (class in pytorch_forecasting.utils._utils) integer_histogram() (in module pytorch_forecasting.utils._utils) interpret_output() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) InterpretableMultiHeadAttention (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) inverse_preprocess() (pytorch_forecasting.data.encoders.TransformMixIn method) inverse_transform() (pytorch_forecasting.data.encoders.GroupNormalizer method) (pytorch_forecasting.data.encoders.NaNLabelEncoder method) (pytorch_forecasting.data.encoders.TorchNormalizer method) is_numeric() (pytorch_forecasting.data.encoders.NaNLabelEncoder static method)

L

lagged_target_positions (pytorch_forecasting.models.base._base_model.AutoRegressiveBaseModel property) (pytorch_forecasting.models.base._base_model.AutoRegressiveBaseModelWithCovariates property) lagged_targets (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) lagged_variables (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) linear() (in module pytorch_forecasting.models.nbeats.sub_modules) linspace() (in module pytorch_forecasting.models.nbeats.sub_modules) load() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet class method) load_toydata() (in module pytorch_forecasting.data.examples) log() (pytorch_forecasting.models.base._base_model.BaseModel method) log_abs_det_jacobian() (pytorch_forecasting.data.encoders.MinusOneTransform method) (pytorch_forecasting.data.encoders.ReLuTransform method) (pytorch_forecasting.data.encoders.SoftplusTransform method) log_embeddings() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) log_gradient_flow() (pytorch_forecasting.models.base._base_model.BaseModel method) log_interpretation() (pytorch_forecasting.models.nbeats._nbeats.NBeats method) (pytorch_forecasting.models.nhits._nhits.NHiTS method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) log_interval (pytorch_forecasting.models.base._base_model.BaseModel property) log_metrics() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method)

log_prediction() (pytorch_forecasting.models.base._base_model.BaseModel method) log_prob() (pytorch_forecasting.metrics._mqf2_utils.MQF2Distribution method) (pytorch_forecasting.metrics._mqf2_utils.TransformedMQF2Distribution method) LogNormalDistributionLoss (class in pytorch_forecasting.metrics.distributions) loss() (pytorch_forecasting.metrics.base_metrics.DistributionLoss method) (pytorch_forecasting.metrics.base_metrics.MultiHorizonMetric method) (pytorch_forecasting.metrics.base_metrics.MultivariateDistributionLoss method) (pytorch_forecasting.metrics.distributions.BetaDistributionLoss method) (pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss method) (pytorch_forecasting.metrics.distributions.MQF2DistributionLoss 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) LSTM (class in pytorch_forecasting.models.nn.rnn)

M

MAE (class in pytorch_forecasting.metrics.point) map_x_to_distribution() (pytorch_forecasting.metrics.base_metrics.DistributionLoss method) (pytorch_forecasting.metrics.distributions.BetaDistributionLoss 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) MAPE (class in pytorch_forecasting.metrics.point) MASE (class in pytorch_forecasting.metrics.point) mask_losses() (pytorch_forecasting.metrics.base_metrics.MultiHorizonMetric method) masked_op() (in module pytorch_forecasting.utils._utils) max_lag (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) metadata (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule property) Metric (class in pytorch_forecasting.metrics.base_metrics) min_lag (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) MinusOneTransform (class in pytorch_forecasting.data.encoders) MLP (class in pytorch_forecasting.models.nhits.sub_modules) module pytorch_forecasting.data pytorch_forecasting.data.data_module pytorch_forecasting.data.encoders pytorch_forecasting.data.examples pytorch_forecasting.data.samplers pytorch_forecasting.data.timeseries pytorch_forecasting.data.timeseries._timeseries pytorch_forecasting.data.timeseries._timeseries_v2 pytorch_forecasting.metrics pytorch_forecasting.metrics._mqf2_utils pytorch_forecasting.metrics.base_metrics pytorch_forecasting.metrics.distributions pytorch_forecasting.metrics.point pytorch_forecasting.metrics.quantile pytorch_forecasting.models pytorch_forecasting.models.base pytorch_forecasting.models.base._base_model pytorch_forecasting.models.base._base_model_v2 pytorch_forecasting.models.base._base_object pytorch_forecasting.models.base_model pytorch_forecasting.models.baseline pytorch_forecasting.models.deepar pytorch_forecasting.models.deepar._deepar pytorch_forecasting.models.deepar._deepar_pkg pytorch_forecasting.models.mlp pytorch_forecasting.models.mlp._decodermlp pytorch_forecasting.models.mlp._decodermlp_pkg pytorch_forecasting.models.mlp.submodules pytorch_forecasting.models.nbeats pytorch_forecasting.models.nbeats._nbeats pytorch_forecasting.models.nbeats._nbeats_pkg pytorch_forecasting.models.nbeats.sub_modules pytorch_forecasting.models.nhits pytorch_forecasting.models.nhits._nhits pytorch_forecasting.models.nhits.sub_modules pytorch_forecasting.models.nn pytorch_forecasting.models.nn.embeddings pytorch_forecasting.models.nn.rnn pytorch_forecasting.models.rnn pytorch_forecasting.models.rnn._rnn pytorch_forecasting.models.temporal_fusion_transformer pytorch_forecasting.models.temporal_fusion_transformer._tft pytorch_forecasting.models.temporal_fusion_transformer._tft_v2 pytorch_forecasting.models.temporal_fusion_transformer.sub_modules pytorch_forecasting.models.temporal_fusion_transformer.tuning pytorch_forecasting.models.tide pytorch_forecasting.models.tide._tide pytorch_forecasting.models.tide._tide_pkg pytorch_forecasting.models.tide.sub_modules pytorch_forecasting.models.timexer pytorch_forecasting.models.timexer._timexer pytorch_forecasting.models.timexer._timexer_pkg pytorch_forecasting.models.timexer.sub_modules pytorch_forecasting.utils pytorch_forecasting.utils._classproperty pytorch_forecasting.utils._coerce pytorch_forecasting.utils._dependencies pytorch_forecasting.utils._dependencies._dependencies pytorch_forecasting.utils._dependencies._safe_import pytorch_forecasting.utils._dependencies.tests pytorch_forecasting.utils._dependencies.tests.test_safe_import pytorch_forecasting.utils._maint pytorch_forecasting.utils._maint._show_versions pytorch_forecasting.utils._utils

move_to_device() (in module pytorch_forecasting.utils._utils) MQF2Distribution (class in pytorch_forecasting.metrics._mqf2_utils) MQF2DistributionLoss (class in pytorch_forecasting.metrics.distributions) multi_target (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) MultiEmbedding (class in pytorch_forecasting.models.nn.embeddings) MultiHorizonMetric (class in pytorch_forecasting.metrics.base_metrics) MultiLoss (class in pytorch_forecasting.metrics.base_metrics) MultiNormalizer (class in pytorch_forecasting.data.encoders) MultivariateDistributionLoss (class in pytorch_forecasting.metrics.base_metrics) MultivariateNormalDistributionLoss (class in pytorch_forecasting.metrics.distributions)

N

n_stacks (pytorch_forecasting.models.nhits._nhits.NHiTS property) n_targets (pytorch_forecasting.models.base._base_model.BaseModel property) name() (pytorch_forecasting.models.base._base_object._BasePtForecaster class method) names (pytorch_forecasting.data.encoders.GroupNormalizer property) NaNLabelEncoder (class in pytorch_forecasting.data.encoders) NBeats (class in pytorch_forecasting.models.nbeats._nbeats) NBeats_pkg (class in pytorch_forecasting.models.nbeats._nbeats_pkg) NBEATSBlock (class in pytorch_forecasting.models.nbeats.sub_modules)

NBEATSGenericBlock (class in pytorch_forecasting.models.nbeats.sub_modules) NBEATSSeasonalBlock (class in pytorch_forecasting.models.nbeats.sub_modules) NBEATSTrendBlock (class in pytorch_forecasting.models.nbeats.sub_modules) NegativeBinomialDistributionLoss (class in pytorch_forecasting.metrics.distributions) next_fast_len() (in module pytorch_forecasting.utils._utils) NHiTS (class in pytorch_forecasting.models.nhits._nhits) (class in pytorch_forecasting.models.nhits.sub_modules) NHiTSBlock (class in pytorch_forecasting.models.nhits.sub_modules) NormalDistributionLoss (class in pytorch_forecasting.metrics.distributions)

O

on_after_backward() (pytorch_forecasting.models.base._base_model.BaseModel method) on_epoch_end() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) on_fit_end() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) on_load_checkpoint() (pytorch_forecasting.models.base._base_model.BaseModel method) on_predict_batch_end() (pytorch_forecasting.models.base._base_model.PredictCallback method) on_predict_epoch_end() (pytorch_forecasting.models.base._base_model.PredictCallback method)

on_save_checkpoint() (pytorch_forecasting.models.base._base_model.BaseModel method) on_test_epoch_end() (pytorch_forecasting.models.base._base_model.BaseModel method) on_train_epoch_end() (pytorch_forecasting.models.base._base_model.BaseModel method) on_validation_epoch_end() (pytorch_forecasting.models.base._base_model.BaseModel method) optimize_hyperparameters() (in module pytorch_forecasting.models.temporal_fusion_transformer.tuning) output_to_prediction() (pytorch_forecasting.models.base._base_model.AutoRegressiveBaseModel method) OutputMixIn (class in pytorch_forecasting.utils._utils)

P

padded_stack() (in module pytorch_forecasting.utils._utils) persistent() (pytorch_forecasting.metrics.base_metrics.AggregationMetric method) (pytorch_forecasting.metrics.base_metrics.CompositeMetric method) (pytorch_forecasting.metrics.base_metrics.MultiLoss method) (pytorch_forecasting.metrics.base_metrics.TorchMetricWrapper method) plot_interpretation() (pytorch_forecasting.models.nbeats._nbeats.NBeats method) (pytorch_forecasting.models.nhits._nhits.NHiTS method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) plot_prediction() (pytorch_forecasting.models.base._base_model.AutoRegressiveBaseModel method) (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer method) plot_prediction_actual_by_variable() (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates method) plot_randomization() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) PoissonLoss (class in pytorch_forecasting.metrics.point) PositionalEmbedding (class in pytorch_forecasting.models.timexer.sub_modules) PositionalEncoder (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) predict() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.deepar._deepar.DeepAR method) predict_dataloader() (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule method) predict_dependency() (pytorch_forecasting.models.base._base_model.BaseModel method) predict_step() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method) PredictCallback (class in pytorch_forecasting.models.base._base_model) Prediction (class in pytorch_forecasting.models.base._base_model) PredictTuple (in module pytorch_forecasting.models.base._base_model) prepare_data_per_node (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule attribute) preprocess() (pytorch_forecasting.data.encoders.TransformMixIn method) profile() (in module pytorch_forecasting.utils._utils) pytorch_forecasting.data module pytorch_forecasting.data.data_module module pytorch_forecasting.data.encoders module pytorch_forecasting.data.examples module pytorch_forecasting.data.samplers module pytorch_forecasting.data.timeseries module pytorch_forecasting.data.timeseries._timeseries module pytorch_forecasting.data.timeseries._timeseries_v2 module pytorch_forecasting.metrics module pytorch_forecasting.metrics._mqf2_utils module pytorch_forecasting.metrics.base_metrics module pytorch_forecasting.metrics.distributions module pytorch_forecasting.metrics.point module pytorch_forecasting.metrics.quantile module pytorch_forecasting.models module pytorch_forecasting.models.base module pytorch_forecasting.models.base._base_model module pytorch_forecasting.models.base._base_model_v2 module pytorch_forecasting.models.base._base_object module pytorch_forecasting.models.base_model module pytorch_forecasting.models.baseline module pytorch_forecasting.models.deepar module pytorch_forecasting.models.deepar._deepar module pytorch_forecasting.models.deepar._deepar_pkg module pytorch_forecasting.models.mlp module

pytorch_forecasting.models.mlp._decodermlp module pytorch_forecasting.models.mlp._decodermlp_pkg module pytorch_forecasting.models.mlp.submodules module pytorch_forecasting.models.nbeats module pytorch_forecasting.models.nbeats._nbeats module pytorch_forecasting.models.nbeats._nbeats_pkg module pytorch_forecasting.models.nbeats.sub_modules module pytorch_forecasting.models.nhits module pytorch_forecasting.models.nhits._nhits module pytorch_forecasting.models.nhits.sub_modules module pytorch_forecasting.models.nn module pytorch_forecasting.models.nn.embeddings module pytorch_forecasting.models.nn.rnn module pytorch_forecasting.models.rnn module pytorch_forecasting.models.rnn._rnn module pytorch_forecasting.models.temporal_fusion_transformer module pytorch_forecasting.models.temporal_fusion_transformer._tft module pytorch_forecasting.models.temporal_fusion_transformer._tft_v2 module pytorch_forecasting.models.temporal_fusion_transformer.sub_modules module pytorch_forecasting.models.temporal_fusion_transformer.tuning module pytorch_forecasting.models.tide module pytorch_forecasting.models.tide._tide module pytorch_forecasting.models.tide._tide_pkg module pytorch_forecasting.models.tide.sub_modules module pytorch_forecasting.models.timexer module pytorch_forecasting.models.timexer._timexer module pytorch_forecasting.models.timexer._timexer_pkg module pytorch_forecasting.models.timexer.sub_modules module 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._safe_import module pytorch_forecasting.utils._dependencies.tests module pytorch_forecasting.utils._dependencies.tests.test_safe_import module pytorch_forecasting.utils._maint module pytorch_forecasting.utils._maint._show_versions module pytorch_forecasting.utils._utils module

Q

quantile() (pytorch_forecasting.metrics._mqf2_utils.MQF2Distribution method)

QuantileLoss (class in pytorch_forecasting.metrics.quantile)

R

reals (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates property) RecurrentNetwork (class in pytorch_forecasting.models.rnn._rnn) reduce_loss() (pytorch_forecasting.metrics.base_metrics.MultiHorizonMetric method) ReLuTransform (class in pytorch_forecasting.data.encoders) repeat_interleave() (pytorch_forecasting.models.nn.rnn.GRU method) (pytorch_forecasting.models.nn.rnn.LSTM method) (pytorch_forecasting.models.nn.rnn.RNN method) repr_class() (in module pytorch_forecasting.utils._utils) ResampleNorm (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) rescale_parameters() (pytorch_forecasting.metrics.base_metrics.Metric 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)

reset() (pytorch_forecasting.metrics.base_metrics.AggregationMetric method) (pytorch_forecasting.metrics.base_metrics.CompositeMetric method) (pytorch_forecasting.metrics.base_metrics.MultiLoss method) (pytorch_forecasting.metrics.base_metrics.TorchMetricWrapper method) reset_overwrite_values() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) RMSE (class in pytorch_forecasting.metrics.point) RNN (class in pytorch_forecasting.models.nn.rnn) rsample() (pytorch_forecasting.metrics._mqf2_utils.MQF2Distribution method)

S

sample() (pytorch_forecasting.metrics.base_metrics.DistributionLoss method) (pytorch_forecasting.metrics.base_metrics.MultivariateDistributionLoss method) (pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss method) save() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) ScaledDotProductAttention (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) SequentialNet (class in pytorch_forecasting.metrics._mqf2_utils) set_fit_request() (pytorch_forecasting.data.encoders.NaNLabelEncoder method) set_overwrite_values() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) set_transform_request() (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) setup() (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule method)

show_versions() (in module pytorch_forecasting.utils._maint._show_versions) size() (pytorch_forecasting.models.base._base_model.BaseModel method) SMAPE (class in pytorch_forecasting.metrics.point) softplus_inv() (in module pytorch_forecasting.data.encoders) SoftplusTransform (class in pytorch_forecasting.data.encoders) stack_sliding_view() (pytorch_forecasting.metrics._mqf2_utils.MQF2Distribution method) static_size (pytorch_forecasting.models.nhits._nhits.NHiTS property) (pytorch_forecasting.models.tide._tide.TiDEModel property) (pytorch_forecasting.models.timexer._timexer.TimeXer property) static_variables (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates property) StaticFeaturesEncoder (class in pytorch_forecasting.models.nhits.sub_modules) step() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.nbeats._nbeats.NBeats method) (pytorch_forecasting.models.nhits._nhits.NHiTS method)

T

target_names (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) (pytorch_forecasting.models.base._base_model.BaseModel property) target_normalizers (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) target_positions (pytorch_forecasting.models.base._base_model.AutoRegressiveBaseModel property) (pytorch_forecasting.models.base._base_model.BaseModelWithCovariates property) TemporalFusionTransformer (class in pytorch_forecasting.models.temporal_fusion_transformer._tft) test_dataloader() (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule method) 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_present_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) test_step() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method) TFT (class in pytorch_forecasting.models.temporal_fusion_transformer._tft_v2) TiDEModel (class in pytorch_forecasting.models.tide._tide) TiDEModel_pkg (class in pytorch_forecasting.models.tide._tide_pkg) TimeDistributed (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) TimeDistributedEmbeddingBag (class in pytorch_forecasting.models.nn.embeddings) TimeDistributedInterpolation (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) TimeSeries (class in pytorch_forecasting.data.timeseries._timeseries_v2) TimeSeriesDataSet (class in pytorch_forecasting.data.timeseries._timeseries) TimeSynchronizedBatchSampler (class in pytorch_forecasting.data.samplers) TimeXer (class in pytorch_forecasting.models.timexer._timexer) TimeXer_pkg (class in pytorch_forecasting.models.timexer._timexer_pkg) to_dataloader() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) to_list() (in module pytorch_forecasting.utils._utils) to_network_output() (pytorch_forecasting.utils._utils.TupleOutputMixIn method) to_prediction() (pytorch_forecasting.metrics.base_metrics.CompositeMetric method) (pytorch_forecasting.metrics.base_metrics.DistributionLoss method) (pytorch_forecasting.metrics.base_metrics.Metric method) (pytorch_forecasting.metrics.base_metrics.MultiLoss method) (pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss method) (pytorch_forecasting.metrics.distributions.NegativeBinomialDistributionLoss method) (pytorch_forecasting.metrics.point.CrossEntropy method) (pytorch_forecasting.metrics.point.PoissonLoss method) (pytorch_forecasting.metrics.point.TweedieLoss method) (pytorch_forecasting.metrics.quantile.QuantileLoss method) (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.baseline.Baseline method)

to_quantiles() (pytorch_forecasting.metrics.base_metrics.CompositeMetric method) (pytorch_forecasting.metrics.base_metrics.DistributionLoss method) (pytorch_forecasting.metrics.base_metrics.Metric method) (pytorch_forecasting.metrics.base_metrics.MultiLoss method) (pytorch_forecasting.metrics.distributions.ImplicitQuantileNetworkDistributionLoss method) (pytorch_forecasting.metrics.distributions.MQF2DistributionLoss method) (pytorch_forecasting.metrics.point.CrossEntropy method) (pytorch_forecasting.metrics.point.PoissonLoss method) (pytorch_forecasting.metrics.quantile.QuantileLoss method) (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.baseline.Baseline method) TorchMetricWrapper (class in pytorch_forecasting.metrics.base_metrics) TorchNormalizer (class in pytorch_forecasting.data.encoders) train_dataloader() (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule method) training_step() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method) transform() (pytorch_forecasting.data.encoders.GroupNormalizer method) (pytorch_forecasting.data.encoders.MultiNormalizer method) (pytorch_forecasting.data.encoders.NaNLabelEncoder method) (pytorch_forecasting.data.encoders.TorchNormalizer method) transform_output() (pytorch_forecasting.models.base._base_model.BaseModel method) transform_values() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method) TransformedMQF2Distribution (class in pytorch_forecasting.metrics._mqf2_utils) TransformMixIn (class in pytorch_forecasting.data.encoders) TriangularCausalMask (class in pytorch_forecasting.models.timexer.sub_modules) 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) update() (pytorch_forecasting.metrics.base_metrics.AggregationMetric method) (pytorch_forecasting.metrics.base_metrics.CompositeMetric method) (pytorch_forecasting.metrics.base_metrics.Metric method) (pytorch_forecasting.metrics.base_metrics.MultiHorizonMetric method) (pytorch_forecasting.metrics.base_metrics.MultiLoss method) (pytorch_forecasting.metrics.base_metrics.TorchMetricWrapper method) (pytorch_forecasting.metrics.point.MASE method)

V

val_dataloader() (pytorch_forecasting.data.data_module.EncoderDecoderTimeSeriesDataModule method) validation_step() (pytorch_forecasting.models.base._base_model.BaseModel method) (pytorch_forecasting.models.base._base_model_v2.BaseModel method)

variable_to_group_mapping (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet property) VariableSelectionNetwork (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules)

W

write_on_batch_end() (pytorch_forecasting.models.base._base_model.PredictCallback method)

write_on_epoch_end() (pytorch_forecasting.models.base._base_model.PredictCallback method)

X

x_to_index() (pytorch_forecasting.data.timeseries._timeseries.TimeSeriesDataSet method)