索引

_

_check_matplotlib() (在模块 pytorch_forecasting.utils._dependencies 中) _clipped_log() (在模块 pytorch_forecasting.data.encoders 中) _clipped_logit() (在模块 pytorch_forecasting.data.encoders 中) _coerce_to_dict() (在模块 pytorch_forecasting.data.timeseries 中) _coerce_to_list() (在模块 pytorch_forecasting.data.timeseries 中) _concatenate_output() (在模块 pytorch_forecasting.models.base_model 中) _find_end_indices() (在模块 pytorch_forecasting.data.timeseries 中) _get_data_by_filename() (在模块 pytorch_forecasting.data.examples 中) _get_deps_info() (在模块 pytorch_forecasting.utils._maint._show_versions 中)

_get_installed_packages() (在模块 pytorch_forecasting.utils._dependencies 中) _get_installed_packages_private() (在模块 pytorch_forecasting.utils._dependencies 中) _get_sys_info() (在模块 pytorch_forecasting.utils._maint._show_versions 中) _identity() (在模块 pytorch_forecasting.data.encoders 中) _minus_one() (在模块 pytorch_forecasting.data.encoders 中) _plus_one() (在模块 pytorch_forecasting.data.encoders 中) _ResidualBlock (类在 pytorch_forecasting.models.tide.sub_modules 中) _square() (在模块 pytorch_forecasting.data.encoders 中) _TideModule (类在 pytorch_forecasting.models.tide.sub_modules 中) _torch_cat_na() (在模块 pytorch_forecasting.models.base_model 中)

A

AddNorm (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules 中的类) AggregationMetric (pytorch_forecasting.metrics.base_metrics 中的类) apply_to_list() (pytorch_forecasting.utils._utils 模块中的函数)

autocorrelation() (pytorch_forecasting.utils._utils 模块中的函数) AutoRegressiveBaseModel (pytorch_forecasting.models.base_model 中的类) AutoRegressiveBaseModelWithCovariates (pytorch_forecasting.models.base_model 中的类)

B

Baseline (pytorch_forecasting.models.baseline 中的类) BaseModel (pytorch_forecasting.models.base_model 中的类)

BaseModelWithCovariates (pytorch_forecasting.models.base_model 中的类) BetaDistributionLoss (pytorch_forecasting.metrics.distributions 中的类)

C

calculate_decoder_length() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) calculate_prediction_actual_by_variable() (pytorch_forecasting.models.base_model.BaseModelWithCovariates method) categorical_groups_mapping (pytorch_forecasting.models.base_model.BaseModelWithCovariates property) categoricals (pytorch_forecasting.data.timeseries.TimeSeriesDataSet property) (pytorch_forecasting.models.base_model.BaseModelWithCovariates property) check_for_nonfinite() (in module pytorch_forecasting.data.timeseries) 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_model.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_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) CrossEntropy (class in pytorch_forecasting.metrics.point) current_stage (pytorch_forecasting.models.base_model.BaseModel property)

D

decode() (pytorch_forecasting.models.deepar._deepar.DeepAR 方法) (pytorch_forecasting.models.rnn._rnn.RecurrentNetwork 方法) decode_autoregressive() (pytorch_forecasting.models.base_model.AutoRegressiveBaseModel 方法) decoded_index (pytorch_forecasting.data.timeseries.TimeSeriesDataSet 属性) decoder_covariate_size (pytorch_forecasting.models.nhits._nhits.NHiTS 属性) (pytorch_forecasting.models.tide._tide.TiDEModel 属性) decoder_variables (pytorch_forecasting.models.base_model.BaseModelWithCovariates 属性) DecoderMLP (pytorch_forecasting.models.mlp._decodermlp 中的类) deduce_default_output_parameters() (pytorch_forecasting.models.base_model.BaseModel 静态方法) DeepAR (pytorch_forecasting.models.deepar._deepar 中的类)

detach() (在模块 pytorch_forecasting.utils._utils 中) distribution_arguments (pytorch_forecasting.metrics.base_metrics.DistributionLoss 属性) distribution_class (pytorch_forecasting.metrics.base_metrics.DistributionLoss 属性) (pytorch_forecasting.metrics.distributions.BetaDistributionLoss 属性) (pytorch_forecasting.metrics.distributions.LogNormalDistributionLoss 属性) (pytorch_forecasting.metrics.distributions.MultivariateNormalDistributionLoss 属性) (pytorch_forecasting.metrics.distributions.NegativeBinomialDistributionLoss 属性) (pytorch_forecasting.metrics.distributions.NormalDistributionLoss 属性) DistributionLoss (pytorch_forecasting.metrics.base_metrics 中的类) dropout_categoricals (pytorch_forecasting.data.timeseries.TimeSeriesDataSet 属性)

E

encode() (pytorch_forecasting.models.deepar._deepar.DeepAR 方法) (pytorch_forecasting.models.rnn._rnn.RecurrentNetwork 方法) encoder_covariate_size (pytorch_forecasting.models.nhits._nhits.NHiTS 属性) (pytorch_forecasting.models.tide._tide.TiDEModel 属性) encoder_variables (pytorch_forecasting.models.base_model.BaseModelWithCovariates 属性)

EncoderNormalizer (pytorch_forecasting.data.encoders 中的类) expand_static_context() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer 方法) Expm1Transform (pytorch_forecasting.data.encoders 中的类) extra_repr() (pytorch_forecasting.metrics.base_metrics.Metric 方法) extract_features() (pytorch_forecasting.models.base_model.BaseModelWithCovariates 方法)

F

filter() (pytorch_forecasting.data.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.TimeSeriesDataSet property) forward() (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_model.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.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.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)

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.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) from_parameters() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet class method) 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_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.TimeSeriesDataSet method)

get_rnn() (in module pytorch_forecasting.models.nn.rnn) get_stallion_data() (in module pytorch_forecasting.data.examples) get_transform() (pytorch_forecasting.data.encoders.TransformMixIn class method) get_transformer() (pytorch_forecasting.data.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 方法) (pytorch_forecasting.models.nn.rnn.LSTM 方法) (pytorch_forecasting.models.nn.rnn.RNN 方法)

我

IdentityBasis (pytorch_forecasting.models.nhits.sub_modules 中的类) iget() (pytorch_forecasting.utils._utils.OutputMixIn 方法) ImplicitQuantileNetwork (pytorch_forecasting.metrics.distributions 中的类) ImplicitQuantileNetworkDistributionLoss (pytorch_forecasting.metrics.distributions 中的类) init_hidden_state() (pytorch_forecasting.models.nn.rnn.GRU 方法) (pytorch_forecasting.models.nn.rnn.LSTM 方法) (pytorch_forecasting.models.nn.rnn.RNN 方法) init_weights() (在模块 pytorch_forecasting.models.nhits.sub_modules 中)

InitialParameterRepresenterMixIn (pytorch_forecasting.utils._utils 中的类) integer_histogram() (在模块 pytorch_forecasting.utils._utils 中) interpret_output() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer 方法) InterpretableMultiHeadAttention (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules 中的类) inverse_preprocess() (pytorch_forecasting.data.encoders.TransformMixIn 方法) inverse_transform() (pytorch_forecasting.data.encoders.GroupNormalizer 方法) (pytorch_forecasting.data.encoders.NaNLabelEncoder 方法) (pytorch_forecasting.data.encoders.TorchNormalizer 方法) is_numeric() (pytorch_forecasting.data.encoders.NaNLabelEncoder 静态方法)

L

lagged_target_positions (pytorch_forecasting.models.base_model.AutoRegressiveBaseModel property) (pytorch_forecasting.models.base_model.AutoRegressiveBaseModelWithCovariates property) lagged_targets (pytorch_forecasting.data.timeseries.TimeSeriesDataSet property) lagged_variables (pytorch_forecasting.data.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.TimeSeriesDataSet class method) log() (pytorch_forecasting.models.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_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_model.BaseModel property) log_metrics() (pytorch_forecasting.models.base_model.BaseModel method)

log_prediction() (pytorch_forecasting.models.base_model.BaseModel 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.TimeSeriesDataSet property) Metric (class in pytorch_forecasting.metrics.base_metrics) min_lag (pytorch_forecasting.data.timeseries.TimeSeriesDataSet property) MinusOneTransform (class in pytorch_forecasting.data.encoders) module pytorch_forecasting.data pytorch_forecasting.data.encoders pytorch_forecasting.data.examples pytorch_forecasting.data.samplers pytorch_forecasting.data.timeseries pytorch_forecasting.metrics pytorch_forecasting.metrics.base_metrics pytorch_forecasting.metrics.distributions pytorch_forecasting.metrics.point pytorch_forecasting.metrics.quantile pytorch_forecasting.models pytorch_forecasting.models.base_model pytorch_forecasting.models.baseline pytorch_forecasting.models.deepar pytorch_forecasting.models.deepar._deepar pytorch_forecasting.models.mlp pytorch_forecasting.models.mlp._decodermlp pytorch_forecasting.models.mlp.submodules pytorch_forecasting.models.nbeats pytorch_forecasting.models.nbeats._nbeats 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.sub_modules pytorch_forecasting.models.temporal_fusion_transformer.tuning pytorch_forecasting.models.tide pytorch_forecasting.models.tide._tide pytorch_forecasting.models.tide.sub_modules pytorch_forecasting.utils pytorch_forecasting.utils._dependencies 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) multi_target (pytorch_forecasting.data.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 属性) n_targets (pytorch_forecasting.models.base_model.BaseModel 属性) names (pytorch_forecasting.data.encoders.GroupNormalizer 属性) NaNLabelEncoder (pytorch_forecasting.data.encoders 中的类) NBeats (pytorch_forecasting.models.nbeats._nbeats 中的类) NBEATSBlock (pytorch_forecasting.models.nbeats.sub_modules 中的类) NBEATSGenericBlock (pytorch_forecasting.models.nbeats.sub_modules 中的类)

NBEATSSeasonalBlock (pytorch_forecasting.models.nbeats.sub_modules 中的类) NBEATSTrendBlock (pytorch_forecasting.models.nbeats.sub_modules 中的类) NegativeBinomialDistributionLoss (pytorch_forecasting.metrics.distributions 中的类) next_fast_len() (pytorch_forecasting.utils._utils 模块中的函数) NHiTS (pytorch_forecasting.models.nhits._nhits 中的类) (pytorch_forecasting.models.nhits.sub_modules 中的类) NHiTSBlock (pytorch_forecasting.models.nhits.sub_modules 中的类) NormalDistributionLoss (pytorch_forecasting.metrics.distributions 中的类)

O

on_after_backward() (pytorch_forecasting.models.base_model.BaseModel 方法) on_epoch_end() (pytorch_forecasting.models.base_model.BaseModel 方法) (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer 方法) on_fit_end() (pytorch_forecasting.models.temporal_fusion_transformer._tft.TemporalFusionTransformer 方法) on_load_checkpoint() (pytorch_forecasting.models.base_model.BaseModel 方法) on_predict_batch_end() (pytorch_forecasting.models.base_model.PredictCallback 方法) on_predict_epoch_end() (pytorch_forecasting.models.base_model.PredictCallback 方法)

on_save_checkpoint() (pytorch_forecasting.models.base_model.BaseModel 方法) on_test_epoch_end() (pytorch_forecasting.models.base_model.BaseModel 方法) on_train_epoch_end() (pytorch_forecasting.models.base_model.BaseModel 方法) on_validation_epoch_end() (pytorch_forecasting.models.base_model.BaseModel 方法) optimize_hyperparameters() (在模块 pytorch_forecasting.models.temporal_fusion_transformer.tuning 中) output_to_prediction() (pytorch_forecasting.models.base_model.AutoRegressiveBaseModel 方法) OutputMixIn (类在 pytorch_forecasting.utils._utils 中)

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_model.AutoRegressiveBaseModel method) (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.temporal_fusion_transformer._tft.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.point) PositionalEncoder (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) predict() (pytorch_forecasting.models.base_model.BaseModel method) (pytorch_forecasting.models.deepar._deepar.DeepAR method) predict_dependency() (pytorch_forecasting.models.base_model.BaseModel method) predict_step() (pytorch_forecasting.models.base_model.BaseModel method) PredictCallback (class in pytorch_forecasting.models.base_model) Prediction (class in pytorch_forecasting.models.base_model) PredictTuple (in module pytorch_forecasting.models.base_model) preprocess() (pytorch_forecasting.data.encoders.TransformMixIn method) profile() (in module pytorch_forecasting.utils._utils) pytorch_forecasting.data module pytorch_forecasting.data.encoders module pytorch_forecasting.data.examples module pytorch_forecasting.data.samplers module pytorch_forecasting.data.timeseries module pytorch_forecasting.metrics 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_model module pytorch_forecasting.models.baseline module pytorch_forecasting.models.deepar module pytorch_forecasting.models.deepar._deepar module

pytorch_forecasting.models.mlp module pytorch_forecasting.models.mlp._decodermlp module pytorch_forecasting.models.mlp.submodules module pytorch_forecasting.models.nbeats module pytorch_forecasting.models.nbeats._nbeats 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.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.sub_modules module pytorch_forecasting.utils module pytorch_forecasting.utils._dependencies module pytorch_forecasting.utils._maint module pytorch_forecasting.utils._maint._show_versions module pytorch_forecasting.utils._utils module

问

QuantileLoss (pytorch_forecasting.metrics.quantile 中的类)

R

reals (pytorch_forecasting.data.timeseries.TimeSeriesDataSet property) (pytorch_forecasting.models.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.TimeSeriesDataSet method) RMSE (class in pytorch_forecasting.metrics.point) RNN (class in pytorch_forecasting.models.nn.rnn)

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.TimeSeriesDataSet method) ScaledDotProductAttention (class in pytorch_forecasting.models.temporal_fusion_transformer.sub_modules) set_fit_request() (pytorch_forecasting.data.encoders.NaNLabelEncoder method) set_overwrite_values() (pytorch_forecasting.data.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)

show_versions() (in module pytorch_forecasting.utils._maint._show_versions) size() (pytorch_forecasting.models.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) static_size (pytorch_forecasting.models.nhits._nhits.NHiTS property) (pytorch_forecasting.models.tide._tide.TiDEModel property) static_variables (pytorch_forecasting.models.base_model.BaseModelWithCovariates property) StaticFeaturesEncoder (class in pytorch_forecasting.models.nhits.sub_modules) step() (pytorch_forecasting.models.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.TimeSeriesDataSet property) (pytorch_forecasting.models.base_model.BaseModel property) target_normalizers (pytorch_forecasting.data.timeseries.TimeSeriesDataSet property) target_positions (pytorch_forecasting.models.base_model.AutoRegressiveBaseModel property) (pytorch_forecasting.models.base_model.BaseModelWithCovariates property) TemporalFusionTransformer (class in pytorch_forecasting.models.temporal_fusion_transformer._tft) test_step() (pytorch_forecasting.models.base_model.BaseModel method) TiDEModel (class in pytorch_forecasting.models.tide._tide) 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.samplers) to_dataloader() (pytorch_forecasting.data.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_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_model.BaseModel method) (pytorch_forecasting.models.baseline.Baseline method) TorchMetricWrapper (class in pytorch_forecasting.metrics.base_metrics) TorchNormalizer (class in pytorch_forecasting.data.encoders) training_step() (pytorch_forecasting.models.base_model.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_model.BaseModel method) transform_values() (pytorch_forecasting.data.timeseries.TimeSeriesDataSet method) TransformMixIn (class in pytorch_forecasting.data.encoders) TupleOutputMixIn (class in pytorch_forecasting.utils._utils) TweedieLoss (class in pytorch_forecasting.metrics.point)

你

unpack_sequence() (在模块 pytorch_forecasting.utils._utils 中) unsqueeze_like() (在模块 pytorch_forecasting.utils._utils 中) update() (pytorch_forecasting.metrics.base_metrics.AggregationMetric 方法) (pytorch_forecasting.metrics.base_metrics.CompositeMetric 方法) (pytorch_forecasting.metrics.base_metrics.Metric 方法) (pytorch_forecasting.metrics.base_metrics.MultiHorizonMetric 方法) (pytorch_forecasting.metrics.base_metrics.MultiLoss 方法) (pytorch_forecasting.metrics.base_metrics.TorchMetricWrapper 方法) (pytorch_forecasting.metrics.point.MASE 方法)

V

validation_step() (pytorch_forecasting.models.base_model.BaseModel 方法)

variable_to_group_mapping (pytorch_forecasting.data.timeseries.TimeSeriesDataSet 属性) VariableSelectionNetwork (pytorch_forecasting.models.temporal_fusion_transformer.sub_modules 中的类)

W

write_on_batch_end() (pytorch_forecasting.models.base_model.PredictCallback 方法)

write_on_epoch_end() (pytorch_forecasting.models.base_model.PredictCallback 方法)

X

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