PatchTST
The PatchTST model is an efficient Transformer-based model for multivariate time series forecasting.
It is based on two key components: - segmentation of time series into windows (patches) which are served as input tokens to Transformer - channel-independence. where each channel contains a single univariate time series.
1. Backbone
Auxiliary Functions
source
get_activation_fn
source
Transpose
Transpose
Positional Encoding
source
positional_encoding
source
Coord1dPosEncoding
source
Coord2dPosEncoding
source
PositionalEncoding
Encoder
source
TSTEncoderLayer
TSTEncoderLayer
source
TSTEncoder
TSTEncoder
source
TSTiEncoder
TSTiEncoder
source
Flatten_Head
Flatten_Head
source
PatchTST_backbone
PatchTST_backbone
2. Model
source
PatchTST
*PatchTST
The PatchTST model is an efficient Transformer-based model for multivariate time series forecasting.
It is based on two key components: - segmentation of time series into windows (patches) which are served as input tokens to Transformer - channel-independence, where each channel contains a single univariate time series.
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].
stat_exog_list
: str list, static exogenous
columns.
hist_exog_list
: str list, historic exogenous columns.
futr_exog_list
: str list, future exogenous columns.
exclude_insample_y
: bool=False, the model skips the autoregressive
features y[t-input_size:t] if True.
encoder_layers
: int, number
of layers for encoder.
n_heads
: int=16, number of multi-head’s
attention.
hidden_size
: int=128, units of embeddings and
encoders.
linear_hidden_size
: int=256, units of linear layer.
dropout
: float=0.1, dropout rate for residual connection.
fc_dropout
: float=0.1, dropout rate for linear layer.
head_dropout
: float=0.1, dropout rate for Flatten head layer.
attn_dropout
: float=0.1, dropout rate for attention layer.
patch_len
: int=32, length of patch. Note: patch_len = min(patch_len,
input_size + stride).
stride
: int=16, stride of patch.
revin
: bool=True, bool to use RevIn.
revin_affine
: bool=False,
bool to use affine in RevIn.
revin_substract_last
: bool=False,
bool to use substract last in RevIn.
activation
: str=‘ReLU’,
activation from [‘gelu’,‘relu’].
res_attention
: bool=False, bool
to use residual attention.
batch_normalization
: bool=False, bool
to use batch normalization.
learn_pos_embedding
: bool=True, bool
to learn positional embedding.
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=1024, 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.
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, random_seed for pytorch initializer and numpy
generators.
num_workers_loader
: int=os.cpu_count(), workers to be
used by TimeSeriesDataLoader
.
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.
PatchTST.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.
*
PatchTST.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.
**data_module_kwargs
: PL’s
TimeSeriesDataModule args, see
documentation.*