Time-Series Mixer (TSMixer
) is a MLP-based multivariate time-series forecasting model. TSMixer
jointly learns temporal and cross-sectional representations of the time-series by repeatedly combining time- and feature information using stacked mixing layers. A mixing layer consists of a sequential time- and feature Multi Layer Perceptron (MLP
). Note: this model cannot handle exogenous inputs. If you want to use additional exogenous inputs, use TSMixerx
.
MLP
).
MixingLayer
FeatureMixing
TemporalMixing
*TSMixer Time-Series Mixer (
TSMixer
)
is a MLP-based multivariate time-series forecasting model.
TSMixer
jointly learns temporal and cross-sectional representations of the
time-series by repeatedly combining time- and feature information using
stacked mixing layers. A mixing layer consists of a sequential time- and
feature Multi Layer Perceptron
(MLP
).
Parameters:h
: int, forecast horizon.input_size
: int,
considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 ->
lags=[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.exclude_insample_y
: bool=False, if True excludes the target variable
from the input features.n_block
: int=2, number of mixing layers
in the model.ff_dim
: int=64, number of units for the second
feed-forward layer in the feature MLP.dropout
: float=0.9, dropout
rate between (0, 1) .revin
: bool=True, if True uses Reverse
Instance Normalization to process inputs and outputs.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 to
sample in each training batch, default uses all.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.*
predict
method.
cross_validation
to forecast multiple historic values.