Index

_

_find_end_indices() (in module pytorch_forecasting.data.timeseries)

_get_data_by_filename() (in module pytorch_forecasting.data.examples)

A

AddNorm (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) AggregationMetric (class in pytorch_forecasting.metrics)

autocorrelation() (in module pytorch_forecasting.utils) AutoRegressiveBaseModel (class in pytorch_forecasting.models.base_model) AutoRegressiveBaseModelWithCovariates (class in pytorch_forecasting.models.base_model)

B

Baseline (class in pytorch_forecasting.models.baseline)

BaseModel (class in pytorch_forecasting.models.base_model) BaseModelWithCovariates (class in pytorch_forecasting.models.base_model)

C

calculate_prediction_actual_by_variable() (pytorch_forecasting.models.base_model.BaseModelWithCovariates method) categoricals() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet property) CompositeMetric (class in pytorch_forecasting.metrics) compute() (pytorch_forecasting.metrics.AggregationMetric method) (pytorch_forecasting.metrics.CompositeMetric method) (pytorch_forecasting.metrics.Metric method) (pytorch_forecasting.metrics.MultiHorizonMetric method)

configure_optimizers() (pytorch_forecasting.models.base_model.BaseModel method) construct_batch_groups() (pytorch_forecasting.data.timeseries.TimeSynchronizedBatchSampler method) construct_input_vector() (pytorch_forecasting.models.deepar.DeepAR method) CrossEntropy (class in pytorch_forecasting.metrics)

D

decode() (pytorch_forecasting.models.deepar.DeepAR method) DeepAR (class in pytorch_forecasting.models.deepar) distribution_arguments (pytorch_forecasting.metrics.DistributionLoss attribute) distribution_class (pytorch_forecasting.metrics.DistributionLoss attribute) (pytorch_forecasting.metrics.LogNormalDistributionLoss attribute) (pytorch_forecasting.metrics.NegativeBinomialDistributionLoss attribute) (pytorch_forecasting.metrics.NormalDistributionLoss attribute)

DistributionLoss (class in pytorch_forecasting.metrics)

E

encode() (pytorch_forecasting.models.deepar.DeepAR method) EncoderNormalizer (class in pytorch_forecasting.data.encoders)

epoch_end() (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method) expand_static_context() (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method)

F

fit() (pytorch_forecasting.data.encoders.GroupNormalizer 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.TimeSeriesDataSet property) forward() (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.baseline.Baseline method) (pytorch_forecasting.models.deepar.DeepAR method) (pytorch_forecasting.models.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.nn.embeddings.MultiEmbedding method) (pytorch_forecasting.models.nn.embeddings.TimeDistributedEmbeddingBag 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.temporal_fusion_transformer.TemporalFusionTransformer method)

from_dataset() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet class method) (pytorch_forecasting.models.base_model.AutoRegressiveBaseModel class method) (pytorch_forecasting.models.base_model.BaseModel class method) (pytorch_forecasting.models.base_model.BaseModelWithCovariates class method) (pytorch_forecasting.models.deepar.DeepAR class method) (pytorch_forecasting.models.nbeats.NBeats class method) (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer class method) from_parameters() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet class method)

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.TemporalFusionTransformer method) get_embedding_size() (in module pytorch_forecasting.utils)

get_norm() (pytorch_forecasting.data.encoders.GroupNormalizer method) get_parameters() (pytorch_forecasting.data.encoders.GroupNormalizer method) (pytorch_forecasting.data.encoders.TorchNormalizer method) (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) get_stallion_data() (in module pytorch_forecasting.data.examples) groupby_apply() (in module pytorch_forecasting.utils) GroupNormalizer (class in pytorch_forecasting.data.encoders)

I

input_size() (pytorch_forecasting.models.deepar.DeepAR property) integer_histogram() (in module pytorch_forecasting.utils) interpret_output() (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method) InterpretableMultiHeadAttention (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules)

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

linear() (in module pytorch_forecasting.models.nbeats.sub_modules) linspace() (in module pytorch_forecasting.models.nbeats.sub_modules) load() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet class method) log_interval() (pytorch_forecasting.models.base_model.BaseModel method) LogNormalDistributionLoss (class in pytorch_forecasting.metrics) loss() (pytorch_forecasting.metrics.CrossEntropy method) (pytorch_forecasting.metrics.DistributionLoss method) (pytorch_forecasting.metrics.MAE method) (pytorch_forecasting.metrics.MAPE method) (pytorch_forecasting.metrics.MASE method) (pytorch_forecasting.metrics.MultiHorizonMetric method) (pytorch_forecasting.metrics.PoissonLoss method) (pytorch_forecasting.metrics.QuantileLoss method) (pytorch_forecasting.metrics.RMSE method) (pytorch_forecasting.metrics.SMAPE method)

M

MAE (class in pytorch_forecasting.metrics) map_x_to_distribution() (pytorch_forecasting.metrics.DistributionLoss method) (pytorch_forecasting.metrics.LogNormalDistributionLoss method) (pytorch_forecasting.metrics.NegativeBinomialDistributionLoss method) (pytorch_forecasting.metrics.NormalDistributionLoss method) MAPE (class in pytorch_forecasting.metrics) MASE (class in pytorch_forecasting.metrics) mask_losses() (pytorch_forecasting.metrics.MultiHorizonMetric method) Metric (class in pytorch_forecasting.metrics) MetricsCallback (class in pytorch_forecasting.models.temporal_fusion_transformer.tuning) module pytorch_forecasting.data pytorch_forecasting.data.encoders pytorch_forecasting.data.examples pytorch_forecasting.data.timeseries pytorch_forecasting.metrics pytorch_forecasting.models pytorch_forecasting.models.base_model pytorch_forecasting.models.baseline pytorch_forecasting.models.deepar pytorch_forecasting.models.nbeats pytorch_forecasting.models.nbeats.sub_modules pytorch_forecasting.models.nn pytorch_forecasting.models.nn.embeddings pytorch_forecasting.models.temporal_fusion_transformer pytorch_forecasting.models.temporal_fusion_transformer.sub_modules pytorch_forecasting.models.temporal_fusion_transformer.tuning pytorch_forecasting.optim pytorch_forecasting.utils

MultiEmbedding (class in pytorch_forecasting.models.nn.embeddings) MultiHorizonMetric (class in pytorch_forecasting.metrics)

N

names() (pytorch_forecasting.data.encoders.GroupNormalizer property) NaNLabelEncoder (class in pytorch_forecasting.data.encoders) NBeats (class in pytorch_forecasting.models.nbeats) 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) next_fast_len() (in module pytorch_forecasting.utils) NormalDistributionLoss (class in pytorch_forecasting.metrics)

O

on_after_backward() (pytorch_forecasting.models.base_model.BaseModel method) on_fit_end() (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method) on_load_checkpoint() (pytorch_forecasting.models.base_model.BaseModel method)

on_save_checkpoint() (pytorch_forecasting.models.base_model.BaseModel method) on_validation_end() (pytorch_forecasting.models.temporal_fusion_transformer.tuning.MetricsCallback method) optimize_hyperparameters() (in module pytorch_forecasting.models.temporal_fusion_transformer.tuning)

P

padded_stack() (in module pytorch_forecasting.utils) plot_interpretation() (pytorch_forecasting.models.nbeats.NBeats method) (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method) plot_prediction() (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.deepar.DeepAR method) (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method) plot_prediction_actual_by_variable() (pytorch_forecasting.models.base_model.BaseModelWithCovariates method) plot_randomization() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) PoissonLoss (class in pytorch_forecasting.metrics) PositionalEncoder (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) predict() (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.deepar.DeepAR method) predict_dependency() (pytorch_forecasting.models.base_model.BaseModel method) profile() (in module pytorch_forecasting.utils) pytorch_forecasting.data module pytorch_forecasting.data.encoders module pytorch_forecasting.data.examples module pytorch_forecasting.data.timeseries module pytorch_forecasting.metrics module pytorch_forecasting.models module

pytorch_forecasting.models.base_model module pytorch_forecasting.models.baseline module pytorch_forecasting.models.deepar module pytorch_forecasting.models.nbeats module pytorch_forecasting.models.nbeats.sub_modules module pytorch_forecasting.models.nn module pytorch_forecasting.models.nn.embeddings module pytorch_forecasting.models.temporal_fusion_transformer module pytorch_forecasting.models.temporal_fusion_transformer.sub_modules module pytorch_forecasting.models.temporal_fusion_transformer.tuning module pytorch_forecasting.optim module pytorch_forecasting.utils module

Q

QuantileLoss (class in pytorch_forecasting.metrics)

R

Ranger (class in pytorch_forecasting.optim) reals() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet property) reduce_loss() (pytorch_forecasting.metrics.MultiHorizonMetric method) ResampleNorm (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) rescale_parameters() (pytorch_forecasting.metrics.DistributionLoss method) (pytorch_forecasting.metrics.LogNormalDistributionLoss method) (pytorch_forecasting.metrics.NegativeBinomialDistributionLoss method) (pytorch_forecasting.metrics.NormalDistributionLoss method)

reset_overwrite_values() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) RMSE (class in pytorch_forecasting.metrics)

S

sample_n() (pytorch_forecasting.metrics.DistributionLoss method) save() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) ScaledDotProductAttention (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) set_overwrite_values() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) size() (pytorch_forecasting.models.base_model.BaseModel method)

SMAPE (class in pytorch_forecasting.metrics) step() (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.nbeats.NBeats method) (pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer method) (pytorch_forecasting.optim.Ranger method)

T

TemporalFusionTransformer (class in pytorch_forecasting.models.temporal_fusion_transformer) 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) TimeSeriesDataSet (class in pytorch_forecasting.data.timeseries) TimeSynchronizedBatchSampler (class in pytorch_forecasting.data.timeseries) to_dataloader() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) to_prediction() (pytorch_forecasting.metrics.CompositeMetric method) (pytorch_forecasting.metrics.DistributionLoss method) (pytorch_forecasting.metrics.Metric method) (pytorch_forecasting.metrics.PoissonLoss method)

to_quantiles() (pytorch_forecasting.metrics.CompositeMetric method) (pytorch_forecasting.metrics.DistributionLoss method) (pytorch_forecasting.metrics.Metric method) (pytorch_forecasting.metrics.PoissonLoss method) TorchNormalizer (class in pytorch_forecasting.data.encoders) training_epoch_end() (pytorch_forecasting.models.base_model.BaseModel method) training_step() (pytorch_forecasting.models.base_model.BaseModel method) transform() (pytorch_forecasting.data.encoders.GroupNormalizer method) (pytorch_forecasting.data.encoders.NaNLabelEncoder method) (pytorch_forecasting.data.encoders.TorchNormalizer method) transform_values() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method)

U

unpack_sequence() (in module pytorch_forecasting.utils) update() (pytorch_forecasting.metrics.AggregationMetric method) (pytorch_forecasting.metrics.CompositeMetric method) (pytorch_forecasting.metrics.MASE method) (pytorch_forecasting.metrics.Metric method) (pytorch_forecasting.metrics.MultiHorizonMetric method)

V

validation_epoch_end() (pytorch_forecasting.models.base_model.BaseModel method) validation_step() (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.deepar.DeepAR method)

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

X

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