> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nolano.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Supported Models

> Complete list of Time Series Foundation Models available through Nolano API

## 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](https://arxiv.org/abs/2310.08278) |

## Models without Covariates

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](https://arxiv.org/abs/2403.07815)                                                 |
| **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](https://arxiv.org/abs/2505.23719) |
| **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](https://arxiv.org/abs/2401.12345)                                     |

<Note>
  More models coming soon!
</Note>

## Model Performance

Each model is optimized for different scenarios:

* **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