Informer
The Informer model tackles the vanilla Transformer computational complexity challenges for long-horizon forecasting.
The architecture has three distinctive features: - A ProbSparse self-attention mechanism with an O time and memory complexity Llog(L). - A self-attention distilling process that prioritizes attention and efficiently handles long input sequences. - An MLP multi-step decoder that predicts long time-series sequences in a single forward operation rather than step-by-step.
The Informer 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.
1. Auxiliary Functions
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ConvLayer
ConvLayer
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ProbAttention
ProbAttention
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ProbMask
ProbMask
2. Informer
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Informer
*Informer
The Informer model utilizes a three-component approach to define its embedding: 1) It employs encoded autoregressive features obtained from a convolution network. 2) It uses window-relative positional embeddings derived from harmonic functions. 3) 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 Informer 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.
distil: bool = True, wether the Informer decoder uses
bottlenecks.
loss: PyTorch module, instantiated train 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.
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.
Informer.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.
*
Informer.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.*