Models
TSMixerx
Time-Series Mixer exogenous (TSMixerx) is a MLP-based multivariate time-series forecasting model, with capability for additional exogenous inputs. TSMixerx 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).
1. Auxiliary Functions
1.1 Mixing layers
A mixing layer consists of a sequential time- and feature Multi Layer Perceptron (MLP).
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MixingLayerWithStaticExogenous
MixingLayerWithStaticExogenous
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MixingLayer
MixingLayer
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FeatureMixing
FeatureMixing
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TemporalMixing
TemporalMixing
1.2 Reversible InstanceNormalization
An Instance Normalization Layer that is reversible, based on this reference implementation.2. Model
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TSMixerx
*TSMixerx Time-Series Mixer exogenous (
TSMixerx)
is a MLP-based multivariate time-series forecasting model, with
capability for additional exogenous inputs.
TSMixerx
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 insample_y from the
model.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=0.0, dropout rate between (0, 1)
.revin: bool=True, if True uses Reverse Instance Normalization on
insample_y and applies it to the 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. 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.References:
- Chen, Si-An, Chun-Liang Li, Nate Yoder, Sercan O. Arik, and Tomas Pfister (2023). “TSMixer: An All-MLP Architecture for Time Series Forecasting.”*
TSMixerx.fit
*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.*
TSMixerx.predict
*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.*
3. Usage Examples
Train model and forecast future values withpredict method.
cross_validation to forecast multiple historic values.