Time-LLM
Time-LLM is a reprogramming framework to repurpose LLMs for general time series forecasting with the backbone language models kept intact. In other words, it transforms a forecasting task into a “language task” that can be tackled by an off-the-shelf LLM.
1. Auxiliary Functions
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ReprogrammingLayer
ReprogrammingLayer
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FlattenHead
FlattenHead
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PatchEmbedding
PatchEmbedding
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TokenEmbedding
TokenEmbedding
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ReplicationPad1d
ReplicationPad1d
2. Model
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TimeLLM
*TimeLLM
Time-LLM is a reprogramming framework to repurpose an off-the-shelf LLM for time series forecasting.
It trains a reprogramming layer that translates the observed series into a language task. This is fed to the LLM and an output projection layer translates the output back to numerical predictions.
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].
patch_len: int=16, length of patch.
stride: int=8, stride of patch.
d_ff: int=128, dimension of
fcn.
top_k: int=5, top tokens to consider.
d_llm: int=768,
hidden dimension of LLM.
# LLama7b:4096; GPT2-small:768;
BERT-base:768 d_model: int=32, dimension of model.
n_heads:
int=8, number of heads in attention layer.
enc_in: int=7, encoder
input size.
dec_in: int=7, decoder input size.
llm = None,
Path to pretrained LLM model to use. If not specified, it will use GPT-2
from https://huggingface.co/openai-community/gpt2”
llm_config =
Deprecated, configuration of LLM. If not specified, it will use the
configuration of GPT-2 from
https://huggingface.co/openai-community/gpt2”
llm_tokenizer =
Deprecated, tokenizer of LLM. If not specified, it will use the GPT-2
tokenizer from https://huggingface.co/openai-community/gpt2”
llm_num_hidden_layers = 32, hidden layers in LLM
llm_output_attention: bool = True, whether to output attention in
encoder.
llm_output_hidden_states: bool = True, whether to output
hidden states.
prompt_prefix: str=None, prompt to inform the LLM
about the dataset.
dropout: float=0.1, dropout rate.
stat_exog_list: str list, static exogenous columns.
hist_exog_list: str list, historic exogenous columns.
futr_exog_list: str list, future exogenous columns.
loss:
PyTorch module, instantiated train loss class from losses
collection.
valid_loss: PyTorch module=loss, instantiated valid loss class from
losses
collection.
learning_rate: float=1e-3, Learning rate between (0, 1).
max_steps: int=1000, maximum number of training steps.
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.
**trainer_kwargs: int,
keyword trainer arguments inherited from PyTorch Lighning’s
trainer.
TimeLLM.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.
*
TimeLLM.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.*