- Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang. “Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting”
1. VanillaTransformer
source
VanillaTransformer
*VanillaTransformer Vanilla Transformer, following implementation of the Informer paper, used as baseline. The architecture has three distinctive features: - Full-attention mechanism with O(L^2) time and memory complexity. - An MLP multi-step decoder that predicts long time-series sequences in a single forward operation rather than step-by-step. The Vanilla Transformer model utilizes a three-component approach to define its embedding: - It employs encoded autoregressive features obtained from a convolution network. - It uses window-relative positional embeddings derived from harmonic functions. - Absolute positional embeddings obtained from calendar features are utilized. Parameters:
h: int, forecast horizon.input_size: int,
maximum sequence length for truncated train backpropagation. 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, whether to exclude the target variable
from the input.decoder_input_size_multiplier: float = 0.5, .hidden_size: int=128, units of embeddings and encoders.dropout:
float (0, 1), dropout throughout Informer architecture.n_head:
int=4, controls number of multi-head’s attention.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.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=‘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.VanillaTransformer.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.*
VanillaTransformer.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.*