Finetuning

Fine-tuning is a powerful process for utilizing TimeGPT more effectively. Foundation models are pre-trained on vast amounts of data, capturing wide-ranging features and patterns. These models can then be specialized for specific contexts or domains. With fine-tuning, the model’s parameters are refined to forecast a new task, allowing it to tailor its vast pre-existing knowledge toward the requirements of the new data. Fine-tuning thus serves as a crucial bridge, linking TimeGPT’s broad capabilities to your tasks specificities. Concretely, the process of fine-tuning consists of performing a certain number of training iterations on your input data minimizing the forecasting error. The forecasts will then be produced with the updated model. To control the number of iterations, use the finetune_steps argument of the forecast method.


import pandas as pd
from nixtlats import TimeGPT

/home/ubuntu/miniconda/envs/nixtlats/lib/python3.11/site-packages/statsforecast/core.py:25: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from tqdm.autonotebook import tqdm


timegpt = TimeGPT(
    # defaults to os.environ.get("TIMEGPT_TOKEN")
    token = 'my_token_provided_by_nixtla'
)

Here’s an example of how to fine-tune TimeGPT:


df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')
df.head()

	timestamp	value
0	1949-01-01	112
1	1949-02-01	118
2	1949-03-01	132
3	1949-04-01	129
4	1949-05-01	121


timegpt_fcst_finetune_df = timegpt.forecast(
    df=df, h=12, finetune_steps=10,
    time_col='timestamp', target_col='value',
)

INFO:nixtlats.timegpt:Validating inputs...
INFO:nixtlats.timegpt:Preprocessing dataframes...
INFO:nixtlats.timegpt:Calling Forecast Endpoint...


timegpt.plot(
    df, timegpt_fcst_finetune_df, 
    time_col='timestamp', target_col='value',
)

In this code, finetune_steps=10 means the model will go through 10 iterations of training on your time series data. Keep in mind that fine-tuning can be a bit of trial and error. You might need to adjust the number of finetune_steps based on your specific needs and the complexity of your data. It’s recommended to monitor the model’s performance during fine-tuning and adjust as needed. Be aware that more finetune_steps may lead to longer training times and could potentially lead to overfitting if not managed properly. Remember, fine-tuning is a powerful feature, but it should be used thoughtfully and carefully.