Forecast
Advanced zero-shot forecasting capabilities for time series data
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.
1. Zero-Shot Forecasting
Zero-shot forecasting is an excellent starting point for quick insights.
For detailed instructions, see: Zero-shot forecasting documentation.
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.
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.
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.