RNN
)
in 1990, where each layer uses the following recurrent transformation:
where , is the hidden state of RNN layer for
time , is the input at time and
is the hidden state of the previous layer at ,
are static exogenous inputs,
historic exogenous, are future exogenous
available at the time of the prediction. The available activations are
tanh
, and relu
. The predictions are obtained by transforming the
hidden states into contexts , that are decoded
and adapted into through MLPs.
References*RNN Multi Layer Elman RNN (RNN), with MLP decoder. The network has
tanh
or
relu
non-linearities, it is trained using ADAM stochastic gradient
descent. The network accepts static, historic and future exogenous data.
Parameters:h
: int, forecast horizon.input_size
: int,
maximum sequence length for truncated train backpropagation. Default -1
uses 3 * horizon inference_input_size
: int, maximum sequence
length for truncated inference. Default None uses input_size
history.h_train
: int, maximum sequence length for truncated train
backpropagation. Default 1.encoder_n_layers
: int=2, number of
layers for the RNN.encoder_hidden_size
: int=200, units for the
RNN’s hidden state size.encoder_activation
: str=tanh
, type of
RNN activation from tanh
or relu
.encoder_bias
: bool=True,
whether or not to use biases b_ih, b_hh within RNN units.encoder_dropout
: float=0., dropout regularization applied to RNN
outputs.context_size
: deprecated.decoder_hidden_size
:
int=200, size of hidden layer for the MLP decoder.decoder_layers
:
int=2, number of layers for the MLP decoder.futr_exog_list
: str
list, future exogenous columns.hist_exog_list
: str list, historic
exogenous columns.stat_exog_list
: str list, static exogenous
columns.exclude_insample_y
: bool=False, whether to exclude the
target variable from the historic exogenous data.recurrent
:
bool=False, whether to produce forecasts recursively (True) or direct
(False).loss
: PyTorch module, instantiated train loss class from
losses
collection.valid_loss
: PyTorch module=loss
, instantiated valid loss class from
losses
collection.max_steps
: int=1000, maximum number of training steps.learning_rate
: float=1e-3, Learning rate between (0, 1).num_lr_decays
: int=-1, Number of learning rate decays, evenly
distributed across max_steps.early_stop_patience_steps
: int=-1,
Number of validation iterations before early stopping.val_check_steps
: int=100, Number of training steps between every
validation loss check.batch_size
: int=32, number of
differentseries in each batch.valid_batch_size
: int=None, number
of different series in each validation and test batch.windows_batch_size
: int=128, number of windows to sample in each
training batch, default uses all.inference_windows_batch_size
:
int=1024, number of windows to sample in each inference batch, -1 uses
all.start_padding_enabled
: bool=False, if True, the model will
pad the time series with zeros at the beginning, by input size.step_size
: int=1, step size between each window of temporal
data.scaler_type
: str=‘robust’, type of scaler for temporal inputs
normalization see temporal
scalers.random_seed
: int=1, random_seed for pytorch initializer and numpy
generators.drop_last_loader
: bool=False, if True
TimeSeriesDataLoader
drops last non-full batch.alias
: str,
optional, Custom name of the model.optimizer
: Subclass of
‘torch.optim.Optimizer’, optional, user specified optimizer instead of
the default choice (Adam).optimizer_kwargs
: dict, optional, list
of parameters used by the user specified optimizer
.lr_scheduler
: Subclass of ‘torch.optim.lr_scheduler.LRScheduler’,
optional, user specified lr_scheduler instead of the default choice
(StepLR).lr_scheduler_kwargs
: dict, optional, list of parameters
used by the user specified lr_scheduler
.dataloader_kwargs
: dict, optional, list of parameters passed into the
PyTorch Lightning dataloader by the TimeSeriesDataLoader
. **trainer_kwargs
: int, keyword trainer arguments inherited from
PyTorch Lighning’s
trainer.*Fit. The
fit
method, optimizes the neural network’s weights using the
initialization parameters (learning_rate
, windows_batch_size
, …) and
the loss
function as defined during the initialization. Within fit
we use a PyTorch Lightning Trainer
that inherits the initialization’s
self.trainer_kwargs
, to customize its inputs, see PL’s trainer
arguments.
The method is designed to be compatible with SKLearn-like classes and in
particular to be compatible with the StatsForecast library.
By default the model
is not saving training checkpoints to protect
disk memory, to get them change enable_checkpointing=True
in
__init__
.
Parameters:dataset
: NeuralForecast’s
TimeSeriesDataset
,
see
documentation.val_size
: int, validation size for temporal cross-validation.random_seed
: int=None, random_seed for pytorch initializer and numpy
generators, overwrites model.__init__’s.test_size
: int, test
size for temporal cross-validation.*Predict. Neural network prediction with PL’s
Trainer
execution of
predict_step
.
Parameters:dataset
: NeuralForecast’s
TimeSeriesDataset
,
see
documentation.test_size
: int=None, test size for temporal cross-validation.step_size
: int=1, Step size between each window.random_seed
:
int=None, random_seed for pytorch initializer and numpy generators,
overwrites model.__init__’s.quantiles
: list of floats,
optional (default=None), target quantiles to predict. **data_module_kwargs
: PL’s TimeSeriesDataModule args, see
documentation.*