"""
Module including wrappers for PyTorch implementations of models in GluonTS
"""
import logging
from typing import Any, Dict, Type
from gluonts.model.estimator import Estimator as GluonTSEstimator
from autogluon.timeseries.models.gluonts.abstract_gluonts import AbstractGluonTSModel
from autogluon.timeseries.utils.datetime import (
get_lags_for_frequency,
get_seasonality,
get_time_features_for_frequency,
)
# NOTE: We avoid imports for torch and lightning.pytorch at the top level and hide them inside class methods.
# This is done to skip these imports during multiprocessing (which may cause bugs)
# FIXME: introduces cpflows dependency. We exclude this model until a future release.
# from gluonts.torch.model.mqf2 import MQF2MultiHorizonEstimator
# FIXME: DeepNPTS does not implement the GluonTS PyTorch API, and does not use
# PyTorch Lightning. We exclude this model until a future release.
# from gluonts.torch.model.deep_npts import DeepNPTSEstimator
logger = logging.getLogger(__name__)
[docs]
class DeepARModel(AbstractGluonTSModel):
"""Autoregressive forecasting model based on a recurrent neural network [Salinas2020]_.
Based on `gluonts.torch.model.deepar.DeepAREstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.deepar.html>`_.
See GluonTS documentation for additional hyperparameters.
References
----------
.. [Salinas2020] Salinas, David, et al.
"DeepAR: Probabilistic forecasting with autoregressive recurrent networks."
International Journal of Forecasting. 2020.
Other Parameters
----------------
context_length : int, default = max(10, 2 * prediction_length)
Number of steps to unroll the RNN for before computing predictions
disable_static_features : bool, default = False
If True, static features won't be used by the model even if they are present in the dataset.
If False, static features will be used by the model if they are present in the dataset.
disable_known_covariates : bool, default = False
If True, known covariates won't be used by the model even if they are present in the dataset.
If False, known covariates will be used by the model if they are present in the dataset.
num_layers : int, default = 2
Number of RNN layers
hidden_size : int, default = 40
Number of RNN cells for each layer
dropout_rate : float, default = 0.1
Dropout regularization parameter
embedding_dimension : int, optional
Dimension of the embeddings for categorical features
(if None, defaults to [min(50, (cat+1)//2) for cat in cardinality])
distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
Distribution to use to evaluate observations and sample predictions
scaling: bool, default = True
Whether to automatically scale the target values
max_epochs : int, default = 100
Number of epochs the model will be trained for
batch_size : int, default = 64
Size of batches used during training
predict_batch_size : int, default = 500
Size of batches used during prediction.
num_batches_per_epoch : int, default = 50
Number of batches processed every epoch
lr : float, default = 1e-3,
Learning rate used during training
trainer_kwargs : dict, optional
Optional keyword arguments passed to ``lightning.Trainer``.
early_stopping_patience : int or None, default = 20
Early stop training if the validation loss doesn't improve for this many epochs.
"""
supports_known_covariates = True
def _get_estimator_class(self) -> Type[GluonTSEstimator]:
from gluonts.torch.model.deepar import DeepAREstimator
return DeepAREstimator
def _get_estimator_init_args(self) -> Dict[str, Any]:
init_kwargs = super()._get_estimator_init_args()
init_kwargs["num_feat_static_cat"] = self.num_feat_static_cat
init_kwargs["num_feat_static_real"] = self.num_feat_static_real
init_kwargs["cardinality"] = self.feat_static_cat_cardinality
init_kwargs["num_feat_dynamic_real"] = self.num_feat_dynamic_real
init_kwargs.setdefault("lags_seq", get_lags_for_frequency(self.freq))
init_kwargs.setdefault("time_features", get_time_features_for_frequency(self.freq))
return init_kwargs
[docs]
class SimpleFeedForwardModel(AbstractGluonTSModel):
"""Simple feedforward neural network that simultaneously predicts all future values.
Based on `gluonts.torch.model.simple_feedforward.SimpleFeedForwardEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.simple_feedforward.html>`_.
See GluonTS documentation for additional hyperparameters.
Other Parameters
----------------
context_length : int, default = max(10, 2 * prediction_length)
Number of time units that condition the predictions
hidden_dimensions: List[int], default = [20, 20]
Size of hidden layers in the feedforward network
distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
Distribution to fit.
batch_normalization : bool, default = False
Whether to use batch normalization
mean_scaling : bool, default = True
Scale the network input by the data mean and the network output by its inverse
max_epochs : int, default = 100
Number of epochs the model will be trained for
batch_size : int, default = 64
Size of batches used during training
predict_batch_size : int, default = 500
Size of batches used during prediction.
num_batches_per_epoch : int, default = 50
Number of batches processed every epoch
lr : float, default = 1e-3,
Learning rate used during training
trainer_kwargs : dict, optional
Optional keyword arguments passed to ``lightning.Trainer``.
early_stopping_patience : int or None, default = 20
Early stop training if the validation loss doesn't improve for this many epochs.
"""
def _get_estimator_class(self) -> Type[GluonTSEstimator]:
from gluonts.torch.model.simple_feedforward import SimpleFeedForwardEstimator
return SimpleFeedForwardEstimator
[docs]
class DLinearModel(AbstractGluonTSModel):
"""Simple feedforward neural network that subtracts trend before forecasting [Zeng2023]_.
Based on `gluonts.torch.model.d_linear.DLinearEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.d_linear.html>`_.
See GluonTS documentation for additional hyperparameters.
References
----------
.. [Zeng2023] Zeng, Ailing, et al.
"Are transformers effective for time series forecasting?"
AAAI Conference on Artificial Intelligence. 2023.
Other Parameters
----------------
context_length : int, default = 96
Number of time units that condition the predictions
hidden_dimension: int, default = 20
Size of hidden layers in the feedforward network
distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
Distribution to fit.
scaling : {"mean", "std", None}, default = "mean"
Scaling applied to the inputs. One of ``"mean"`` (mean absolute scaling), ``"std"`` (standardization), ``None`` (no scaling).
max_epochs : int, default = 100
Number of epochs the model will be trained for
batch_size : int, default = 64
Size of batches used during training
predict_batch_size : int, default = 500
Size of batches used during prediction.
num_batches_per_epoch : int, default = 50
Number of batches processed every epoch
lr : float, default = 1e-3,
Learning rate used during training
trainer_kwargs : dict, optional
Optional keyword arguments passed to ``lightning.Trainer``.
early_stopping_patience : int or None, default = 20
Early stop training if the validation loss doesn't improve for this many epochs.
weight_decay : float, default = 1e-8
Weight decay regularization parameter.
"""
@property
def default_context_length(self) -> int:
return 96
def _get_estimator_class(self) -> Type[GluonTSEstimator]:
from gluonts.torch.model.d_linear import DLinearEstimator
return DLinearEstimator
[docs]
class PatchTSTModel(AbstractGluonTSModel):
"""Transformer-based forecaster that segments each time series into patches [Nie2023]_.
Based on `gluonts.torch.model.d_linear.PatchTSTEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.patch_tst.html>`_.
See GluonTS documentation for additional hyperparameters.
References
----------
.. [Nie2023] Nie, Yuqi, et al.
"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers."
International Conference on Learning Representations. 2023.
Other Parameters
----------------
context_length : int, default = 96
Number of time units that condition the predictions
patch_len : int, default = 16
Length of the patch.
stride : int, default = 8
Stride of the patch.
d_model : int, default = 32
Size of hidden layers in the Transformer encoder.
nhead : int, default = 4
Number of attention heads in the Transformer encoder which must divide d_model.
num_encoder_layers : int, default = 2
Number of layers in the Transformer encoder.
distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
Distribution to fit.
scaling : {"mean", "std", None}, default = "mean"
Scaling applied to the inputs. One of ``"mean"`` (mean absolute scaling), ``"std"`` (standardization), ``None`` (no scaling).
max_epochs : int, default = 100
Number of epochs the model will be trained for
batch_size : int, default = 64
Size of batches used during training
num_batches_per_epoch : int, default = 50
Number of batches processed every epoch
lr : float, default = 1e-3,
Learning rate used during training
weight_decay : float, default = 1e-8
Weight decay regularization parameter.
"""
@property
def default_context_length(self) -> int:
return 96
def _get_estimator_class(self) -> Type[GluonTSEstimator]:
from gluonts.torch.model.patch_tst import PatchTSTEstimator
return PatchTSTEstimator
def _get_estimator_init_args(self) -> Dict[str, Any]:
init_kwargs = super()._get_estimator_init_args()
init_kwargs.setdefault("patch_len", 16)
return init_kwargs
[docs]
class WaveNetModel(AbstractGluonTSModel):
"""WaveNet estimator that uses the architecture proposed in [Oord2016]_ with quantized targets.
The model is based on a CNN architecture with dilated convolutions. Time series values are quantized into buckets
and the model is trained using the cross-entropy loss.
Based on `gluonts.torch.model.wavenet.WaveNetEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.wavenet.html>`_.
See GluonTS documentation for additional hyperparameters.
References
----------
.. [Oord2016] Oord, Aaron van den, et al.
"Wavenet: A generative model for raw audio"
arXiv preprint arXiv:1609.03499. 2016.
Other Parameters
----------------
num_bins : int, default = 1024
Number of bins used for quantization of the time series.
num_residual_channels : int, default = 24
Number of residual channels in WaveNet architecture.
num_skip_channels : int, default = 32
Number of skip channels in WaveNet architecture, by default 32
dilation_depth : int or None, default = None
Number of dilation layers in WaveNet architecture. If set to None (default), dilation_depth is set such that
the receptive length is at least as long as the ``seasonality`` and at least ``2 * prediction_length``.
num_stacks : int, default = 1
Number of dilation stacks in WaveNet architecture.
temperature : float, default = 1.0
Temperature used for sampling from softmax distribution.
seasonality : int, optional
The seasonality of the time series. By default is set based on the ``freq`` of the data.
embedding_dimension : int, default = 5
The dimension of the embeddings for categorical features.
use_log_scale_feature : bool, default = True
If True, logarithm of the scale of the past data will be used as an additional static feature.
negative_data : bool, default = True
Flag indicating whether the time series take negative values.
max_epochs : int, default = 100
Number of epochs the model will be trained for
batch_size : int, default = 64
Size of batches used during training
predict_batch_size : int, default = 500
Size of batches used during prediction.
num_batches_per_epoch : int, default = 50
Number of batches processed every epoch
lr : float, default = 1e-3,
Learning rate used during training
trainer_kwargs : dict, optional
Optional keyword arguments passed to ``lightning.Trainer``.
early_stopping_patience : int or None, default = 20
Early stop training if the validation loss doesn't improve for this many epochs.
weight_decay : float, default = 1e-8
Weight decay regularization parameter.
"""
supports_known_covariates = True
default_num_samples: int = 100
def _get_estimator_class(self) -> Type[GluonTSEstimator]:
from gluonts.torch.model.wavenet import WaveNetEstimator
return WaveNetEstimator
def _get_estimator_init_args(self) -> Dict[str, Any]:
init_kwargs = super()._get_estimator_init_args()
init_kwargs["num_feat_static_cat"] = self.num_feat_static_cat
init_kwargs["num_feat_static_real"] = self.num_feat_static_real
init_kwargs["cardinality"] = [1] if self.num_feat_static_cat == 0 else self.feat_static_cat_cardinality
init_kwargs["num_feat_dynamic_real"] = self.num_feat_dynamic_real
init_kwargs.setdefault("negative_data", self.negative_data)
init_kwargs.setdefault("seasonality", get_seasonality(self.freq))
init_kwargs.setdefault("time_features", get_time_features_for_frequency(self.freq))
init_kwargs.setdefault("num_parallel_samples", self.default_num_samples)
# WaveNet model fails if an unsupported frequency such as "SM" is provided. We provide a dummy freq instead
init_kwargs["freq"] = "H"
return init_kwargs