Supported Models

TabPFN-TS: Best overall performance for complex forecasting tasks
chronos-bolt: Fastest inference times for real-time applications
TiRex: Superior performance on seasonal and cyclical data
TOTO: Optimized for observability and monitoring use cases

Models with Covariates Support

These models can incorporate external variables (covariates) to improve forecasting accuracy.

Model	Description	Use Case	Model ID	Paper
Lagllama	Foundation model from Mila Quebec	Best for general forecasting tasks, financial data, and scenarios where external variables significantly impact outcomes. Leverages transformer architecture for complex pattern recognition.	`forecast-model-1`	Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

These models work with univariate time series data and are optimized for specific use cases.

Model	Description	Use Case	Model ID	Paper
chronos-bolt	Amazon’s lightweight Chronos model	Best for real-time forecasting and IoT sensor data requiring low latency predictions. Provides fast inference times with minimal computational overhead.	`forecast-model-2`	Chronos: Learning the Language of Time Series
TiRex	NXAI’s model based on xLSTMs	Best for complex seasonal data and long-term forecasting with intricate cyclical patterns. Designed to capture complex temporal dependencies.	`forecast-model-3`	TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
TOTO	Datadog’s observability-optimized model	Best for DevOps monitoring, infrastructure metrics, and application performance data. Tailored for observability-related forecasting needs.	`forecast-model-4`	TOTO: Time Series Optimized Transformer for Observability

More models coming soon!

Each model is optimized for different scenarios: