TimeGPT offers advanced zero-shot forecasting capabilities for a wide range of time series domains, thanks to its large-scale and diverse pretraining.

Key Feature: Zero-Shot Forecasting

Zero-shot forecasting lets you generate predictions without having to train a new model from scratch on your data. This can significantly reduce your time to production for new or changing forecasting tasks.

Key Feature: Fine-Tuning

Gain performance boosts by fine-tuning TimeGPT on your own dataset or by leveraging specific loss functions. This approach helps tailor the model to your unique forecasting requirements.

By combining zero-shot approaches with optional fine-tuning, TimeGPT offers a robust and efficient solution for time series forecasting.

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1. Zero-Shot Forecasting

Zero-shot forecasting is an excellent starting point for quick insights.

For detailed instructions, see: Zero-shot forecasting documentation.

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2. Add Exogenous Variables

If you have additional external drivers or explanatory factors, include them to improve predictions.

For more details, visit: Forecasting with exogenous variables.

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3. Incorporate Holidays or Special Dates

Holidays and special dates can have significant impact on time series signals.

Learn how to handle them here: Forecasting with holidays and special dates.

Below is a concise code snippet to get started with zero-shot forecasting. This example demonstrates how to import TimeGPT and make a simple prediction.

# Example: Zero-shot forecasting with TimeGPT
from timegpt import TimeGPT

# Initialize TimeGPT
gpt_model = TimeGPT()

# Sample time series data (replace with your own)
time_series_data = [10, 12, 13, 12, 15, 18, 20]

# Make a forecast
forecast = gpt_model.forecast(time_series_data, horizon=3)
print("Forecast:", forecast)

Visual depiction of a sample zero-shot forecast with TimeGPT

Congratulations! You are now equipped with TimeGPT’s key forecasting features. Explore the linked guides for detailed instructions on advanced topics.