Fine-tuning Tutorial TimeGPT

Adapt TimeGPT to your specific datasets for more accurate forecasts

Fine-tuning is a powerful process for utilizing TimeGPT more effectively. Foundation models such as TimeGPT 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 towards 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.

Tutorial

Step 1: Import Packages and Initialize Client

First, we import the required packages and initialize the Nixtla client.

import pandas as pd
from nixtla import NixtlaClient
from utilsforecast.losses import mae, mse
from utilsforecast.evaluation import evaluate

Next, initialize the NixtlaClient instance, providing your API key (or rely on environment variables):

initialize-client

nixtla_client = NixtlaClient(
    api_key='my_api_key_provided_by_nixtla'  # Defaults to os.environ.get("NIXTLA_API_KEY")
)

Step 2: Load Data

Load the dataset from the provided CSV URL:

load-data

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

Step 3: Fine-tune the Model

Set the number of fine-tuning iterations with the finetune_steps parameter. Here, finetune_steps=10 means the model will go through 10 iterations of training on your time series data.

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

Visualize forecasts to confirm performance:

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

Conclusion

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. Usually, a larger value of finetune_steps works better for large datasets.

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.

Additional Resources

• For a detailed guide on using a specific loss function for fine-tuning, check out the Fine-tuning with a specific loss function tutorial.
• Also, read our detailed tutorial on controlling the level of fine-tuning using finetune_depth.