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.

References
- Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen. “Time-LLM: Time Series Forecasting by Reprogramming Large Language Models”

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1. Auxiliary Functions

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Normalize

 Normalize (num_features:int, eps=1e-05, affine=False,
            subtract_last=False, non_norm=False)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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ReprogrammingLayer

 ReprogrammingLayer (d_model, n_heads, d_keys=None, d_llm=None,
                     attention_dropout=0.1)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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FlattenHead

 FlattenHead (n_vars, nf, target_window, head_dropout=0)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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PatchEmbedding

 PatchEmbedding (d_model, patch_len, stride, dropout)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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TokenEmbedding

 TokenEmbedding (c_in, d_model)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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ReplicationPad1d

 ReplicationPad1d (padding)

*Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

.. note:: As per the example above, an __init__() call to the parent class must be made before assignment on the child.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool*

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2. Model

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TimeLLM

 TimeLLM (h, input_size, patch_len:int=16, stride:int=8, d_ff:int=128,
          top_k:int=5, d_llm:int=768, d_model:int=32, n_heads:int=8,
          enc_in:int=7, dec_in:int=7, llm=None, llm_config=None,
          llm_tokenizer=None, llm_num_hidden_layers=32,
          llm_output_attention:bool=True,
          llm_output_hidden_states:bool=True,
          prompt_prefix:Optional[str]=None, dropout:float=0.1,
          stat_exog_list=None, hist_exog_list=None, futr_exog_list=None,
          loss=MAE(), valid_loss=None, learning_rate:float=0.0001,
          max_steps:int=5, val_check_steps:int=100, batch_size:int=32,
          valid_batch_size:Optional[int]=None,
          windows_batch_size:int=1024,
          inference_windows_batch_size:int=1024,
          start_padding_enabled:bool=False, step_size:int=1,
          num_lr_decays:int=0, early_stop_patience_steps:int=-1,
          scaler_type:str='identity', num_workers_loader:int=0,
          drop_last_loader:bool=False, random_seed:int=1, optimizer=None,
          optimizer_kwargs=None, lr_scheduler=None,
          lr_scheduler_kwargs=None, **trainer_kwargs)

*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.
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, LLM model to use. If not specified, it will use GPT-2 from https://huggingface.co/openai-community/gpt2”
llm_config = None, configuration of LLM. If not specified, it will use the configuration of GPT-2 from https://huggingface.co/openai-community/gpt2”
llm_tokenizer = None, 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.

References:
-Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen. “Time-LLM: Time Series Forecasting by Reprogramming Large Language Models”*

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TimeLLM.fit

 TimeLLM.fit (dataset, val_size=0, test_size=0, random_seed=None,
              distributed_config=None)

*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.
*

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TimeLLM.predict

 TimeLLM.predict (dataset, test_size=None, step_size=1, random_seed=None,
                  **data_module_kwargs)

*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.*

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Usage example

from neuralforecast import NeuralForecast
from neuralforecast.models import TimeLLM
from neuralforecast.utils import AirPassengersPanel, augment_calendar_df

from transformers import GPT2Config, GPT2Model, GPT2Tokenizer

AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')

Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test

gpt2_config = GPT2Config.from_pretrained('openai-community/gpt2')
gpt2 = GPT2Model.from_pretrained('openai-community/gpt2', config=gpt2_config)
gpt2_tokenizer = GPT2Tokenizer.from_pretrained('openai-community/gpt2')

prompt_prefix = "The dataset contains data on monthly air passengers. There is a yearly seasonality"

timellm = TimeLLM(h=12,
                 input_size=36,
                 llm=gpt2,
                 llm_config=gpt2_config,
                 llm_tokenizer=gpt2_tokenizer,
                 prompt_prefix=prompt_prefix,
                 batch_size=24,
                 windows_batch_size=24)

nf = NeuralForecast(
    models=[timellm],
    freq='M'
)

nf.fit(df=Y_train_df, val_size=12)
forecasts = nf.predict(futr_df=Y_test_df)

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Tests

try:
    from transformers import GPT2Config, GPT2Model, GPT2Tokenizer
except ImportError:
    raise ImportError('The transformers library is required for Time-LLM to work')

from neuralforecast import NeuralForecast
from neuralforecast.models import TimeLLM

from neuralforecast.utils import AirPassengersPanel, augment_calendar_df

AirPassengersPanel, calendar_cols = augment_calendar_df(df=AirPassengersPanel, freq='M')

Y_train_df = AirPassengersPanel[AirPassengersPanel.ds<AirPassengersPanel['ds'].values[-12]] # 132 train
Y_test_df = AirPassengersPanel[AirPassengersPanel.ds>=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test

gpt2_config = GPT2Config.from_pretrained('openai-community/gpt2')
gpt2 = GPT2Model.from_pretrained('openai-community/gpt2', config=gpt2_config)
gpt2_tokenizer = GPT2Tokenizer.from_pretrained('openai-community/gpt2')

prompt_prefix = "The dataset contains data on monthly air passengers. There is a yearly seasonality"

timellm = TimeLLM(h=12,
                 input_size=36,
                 llm=gpt2,
                 llm_config=gpt2_config,
                 llm_tokenizer=gpt2_tokenizer,
                 prompt_prefix=prompt_prefix,
                 batch_size=24,
                 windows_batch_size=24)

nf = NeuralForecast(
    models=[timellm],
    freq='M'
)

nf.fit(df=Y_train_df, val_size=12)
forecasts = nf.predict(futr_df=Y_test_df)

# Asserts
assert 'TimeLLM' in forecasts.columns, "The column TimeLLM does not exist. Something went wrong with the model"
assert not forecasts['TimeLLM'].isnull().any(), "Predictions contain NaN values."