- Wu, Haixu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. “Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting”
1. Auxiliary Functions
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Decoder
Autoformer decoder
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DecoderLayer
Autoformer decoder layer with the progressive decomposition architecture
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Encoder
Autoformer encoder
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EncoderLayer
Autoformer encoder layer with the progressive decomposition architecture
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LayerNorm
Special designed layernorm for the seasonal part
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AutoCorrelationLayer
Auto Correlation Layer
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AutoCorrelation
AutoCorrelation Mechanism with the following two phases: (1) period-based dependencies discovery (2) time delay aggregation This block can replace the self-attention family mechanism seamlessly.
2. Autoformer
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Autoformer
*Autoformer The Autoformer model tackles the challenge of finding reliable dependencies on intricate temporal patterns of long-horizon forecasting. The architecture has the following distinctive features: - In-built progressive decomposition in trend and seasonal compontents based on a moving average filter. - Auto-Correlation mechanism that discovers the period-based dependencies by calculating the autocorrelation and aggregating similar sub-series based on the periodicity. - Classic encoder-decoder proposed by Vaswani et al. (2017) with a multi-head attention mechanism. The Autoformer model utilizes a three-component approach to define its embedding: - It employs encoded autoregressive features obtained from a convolution network. - Absolute positional embeddings obtained from calendar features are utilized. Parameters:
h: int, forecast horizon.input_size: int,
maximum sequence length for truncated train backpropagation. Default -1
uses all history.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, the model skips the autoregressive
features y[t-input_size:t] if True.decoder_input_size_multiplier: float = 0.5, .hidden_size:
int=128, units of embeddings and encoders.n_head: int=4, controls
number of multi-head’s attention.dropout: float (0, 1), dropout
throughout Autoformer architecture.factor: int=3, Probsparse
attention factor.conv_hidden_size: int=32, channels of the
convolutional encoder.activation: str=GELU, activation from
[‘ReLU’, ‘Softplus’, ‘Tanh’, ‘SELU’, ‘LeakyReLU’, ‘PReLU’, ‘Sigmoid’,
‘GELU’].encoder_layers: int=2, number of layers for the TCN
encoder.decoder_layers: int=1, number of layers for the MLP
decoder.MovingAvg_window: int=25, window size for the moving
average filter.loss: PyTorch module, instantiated train loss
class from losses
collection.valid_loss: PyTorch module, instantiated validation 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.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.Autoformer.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.*
Autoformer.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.*