> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# 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.
**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”](https://arxiv.org/abs/2310.01728) Figure 1. Time-LLM
Architecture.
## 1. Auxiliary Functions
***
source
### ReprogrammingLayer
> ```text theme={null}
> ReprogrammingLayer (d_model, n_heads, d_keys=None, d_llm=None,
> attention_dropout=0.1)
> ```
*ReprogrammingLayer*
***
source
### FlattenHead
> ```text theme={null}
> FlattenHead (n_vars, nf, target_window, head_dropout=0)
> ```
*FlattenHead*
***
source
### PatchEmbedding
> ```text theme={null}
> PatchEmbedding (d_model, patch_len, stride, dropout)
> ```
*PatchEmbedding*
***
source
### TokenEmbedding
> ```text theme={null}
> TokenEmbedding (c_in, d_model)
> ```
*TokenEmbedding*
***
source
### ReplicationPad1d
> ```text theme={null}
> ReplicationPad1d (padding)
> ```
*ReplicationPad1d*
## 2. Model
***
source
### TimeLLM
> ```text theme={null}
> 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', random_seed:int=1,
> drop_last_loader:bool=False, alias:Optional[str]=None,
> optimizer=None, optimizer_kwargs=None, lr_scheduler=None,
> lr_scheduler_kwargs=None, dataloader_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. # 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”](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”](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”](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](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`valid_loss`: PyTorch module=`loss`, instantiated valid loss class from
[losses
collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`learning_rate`: float=1e-3, Learning rate between (0, 1).
`max_steps`: int=1000, maximum number of training steps.
`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. `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.
`scaler_type`: str=‘identity’, type of scaler for temporal inputs
normalization see [temporal
scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).
`random_seed`: int, 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](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=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”](https://arxiv.org/abs/2310.01728)\*
***
### TimeLLM.fit
> ```text theme={null}
> 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](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).
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`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`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
> ```text theme={null}
> TimeLLM.predict (dataset, test_size=None, step_size=1, random_seed=None,
> quantiles=None, **data_module_kwargs)
> ```
\*Predict.
Neural network prediction with PL’s `Trainer` execution of
`predict_step`.
**Parameters:** `dataset`: NeuralForecast’s
[`TimeSeriesDataset`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`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](https://pytorch-lightning.readthedocs.io/en/1.6.1/extensions/datamodules.html#using-a-datamodule).\*
## Usage example
```python theme={null}
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import TimeLLM
from neuralforecast.utils import AirPassengersPanel
Y_train_df = AirPassengersPanel[AirPassengersPanel.ds=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
prompt_prefix = "The dataset contains data on monthly air passengers. There is a yearly seasonality"
timellm = TimeLLM(h=12,
input_size=36,
llm='openai-community/gpt2',
prompt_prefix=prompt_prefix,
batch_size=16,
valid_batch_size=16,
windows_batch_size=16)
nf = NeuralForecast(
models=[timellm],
freq='ME'
)
nf.fit(df=Y_train_df, val_size=12)
forecasts = nf.predict(futr_df=Y_test_df)
```
