> ## Documentation Index > Fetch the complete documentation index at: https://docs.synthefy.com/llms.txt > Use this file to discover all available pages before exploring further. # Forecasting API > Direct access to Synthefy's foundation models for time series forecasting via REST API. The **Forecasting API** provides direct access to Synthefy's foundation models for time series forecasting through a simple REST interface. Deploy on premises with Docker and make HTTP requests to generate accurate forecasts for your time series data. By leveraging foundation models optimized for time series, the API delivers production-grade forecasting capabilities that can be integrated into any application or workflow. ## Key Capabilities Access state-of-the-art foundation models specifically trained for time series forecasting, including the Migas-1.0 model for high-accuracy predictions. Process multiple time series samples in a single request, enabling batch forecasting and scenario comparison across different data streams. Support for various time series formats with automatic handling of timestamps, values, and metadata columns for comprehensive forecasting scenarios. Real-time forecasting capabilities with streaming endpoints for high-throughput applications and real-time decision making. ## Enterprise Features (On Prem) Deploy forecasting models within your own infrastructure with full data privacy and security controls. Containerized deployment with GPU support for high-performance forecasting at scale. Standard HTTP APIs for easy integration with existing systems, monitoring tools, and CI/CD pipelines. ## Getting Started Learn how to deploy the forecasting API and make your first forecast request in minutes. Get help from our support team for custom integrations or enterprise deployment assistance. **Ready to deploy?** Start with our [**Quickstart Guide**](/forecasting-api/quickstart) to get the forecasting API running on premises with Docker.