*Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
to
, etc.
.. note:: As per the example above, an __init__()
call to the parent
class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or
evaluation mode. :vartype training: bool*
*Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
to
, etc.
.. note:: As per the example above, an __init__()
call to the parent
class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or
evaluation mode. :vartype training: bool*
*Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
to
, etc.
.. note:: As per the example above, an __init__()
call to the parent
class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or
evaluation mode. :vartype training: bool*
*Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes::
to
, etc.
.. note:: As per the example above, an __init__()
call to the parent
class must be made before assignment on the child.
:ivar training: Boolean represents whether this module is in training or
evaluation mode. :vartype training: bool*
*TimeXer Parameters:
h
: int, Forecast horizon. input_size
: int,
autorregresive inputs size, y=[1,2,3,4] input_size=2 ->
y_[t-2:t]=[1,2].n_series
: int, number of time-series.futr_exog_list
: str list, future exogenous columns.hist_exog_list
: str list, historic exogenous columns.stat_exog_list
: str list, static exogenous columns.patch_len
:
int, length of patches.hidden_size
: int, dimension of the
model.n_heads
: int, number of heads.e_layers
: int, number
of encoder layers.d_ff
: int, dimension of fully-connected
layer.factor
: int, attention factor.dropout
: float,
dropout rate.use_norm
: bool, whether to normalize or not.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 different
series in each batch.valid_batch_size
: int=None, number of
different series in each validation and test batch, if None uses
batch_size.windows_batch_size
: int=32, number of windows in each
batch.inference_windows_batch_size
: int=32, 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=‘identity’, 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.*