---
warning: DO NOT CHANGE THIS MANUALLY, THIS IS GENERATED BY https://github.com/duckdb/community-extensions repository, check README there
title: timestral
excerpt: |
  DuckDB Community Extensions
  Zero-shot probabilistic time-series forecasting in SQL with Mistral's Timestral models, on CPU via ONNX Runtime

extension:
  name: timestral
  description: Zero-shot probabilistic time-series forecasting in SQL with Mistral's Timestral models, on CPU via ONNX Runtime
  version: '0.1.1'
  language: C++
  build: cmake
  license: MIT
  excluded_platforms: "wasm_mvp;wasm_eh;wasm_threads;windows_amd64_mingw;linux_amd64_musl"
  requires_toolchains: "vcpkg"
  maintainers:
    - redox

repo:
  github: altertable-ai/duckdb-timestral
  ref: 8f0bc7fe5e83d127abd4ea1b4115fa1ab121006b
  variegata: 7d6486900113e4271f8e32324adf27776b63a02b

docs:
  hello_world: |
    INSTALL timestral FROM community;
    LOAD timestral;

    -- Hourly series, one per store. The 5M model (~21 MB) is downloaded and cached on first use.
    CREATE TABLE sales AS
    SELECT g AS store, TIMESTAMP '2026-01-01' + INTERVAL (i) HOUR AS ts, 10 * g + sin(i / 6) AS amount
    FROM range(3) a(g), range(500) b(i);

    -- Forecast the next 24 hours of every store: median plus 21 quantiles per step.
    SELECT store, ts, forecast, q10, q90
    FROM sales TIMESTRAL_FORECAST (amount PARTITION BY store ORDER BY ts HORIZON 24);

    -- The same with the aggregate, for full control over grouping.
    SELECT store, unnest(timestral_forecast(amount ORDER BY ts HORIZON 24), recursive := true)
    FROM sales GROUP BY store;
  extended_description: |
    **timestral** runs [Mistral's Timestral](https://mistral.ai/news/timestral/) zero-shot forecasting models
    inside DuckDB. There is no training step, no Python and no GPU: the network runs on CPU through a statically
    linked ONNX Runtime, and a forecast is one SQL query.

    - `FROM t TIMESTRAL_FORECAST (value PARTITION BY ... ORDER BY time HORIZON n [EVERY step] [USING MODEL size]
      [WITH HISTORY])` returns one row per series and future step: the time, `forecast` (the median) and the 21
      quantiles `q01` to `q99`. The time can be TIMESTAMP, TIMESTAMPTZ, DATE or an integer step. Missing values
      (NULL) and gaps are handled; the step is inferred, or passed as `EVERY INTERVAL '1 month'`.
    - `timestral_forecast(value ORDER BY time HORIZON n ...)` is the aggregate behind it, one list per group.
    - `timestral_models()` lists the four model sizes (5M default, 28M, 123M, 209M) and whether they are cached.
      Set `timestral_model_path` to use a local file offline.

    The forecasts match Mistral's reference PyTorch implementation to within 1e-6 of the series scale.

    **Licence of the model weights.** The extension is MIT licensed. The Timestral weights it downloads are
    Mistral AI's, under Mistral's Modified MIT licence, which **grants no rights to companies whose consolidated
    monthly revenue exceeds $20 million**. Read the licence before using the models, and contact Mistral AI for a
    commercial licence if it applies to you.

extension_star_count: 9
extension_star_count_pretty: 9
extension_download_count: 513
extension_download_count_pretty: 513
image: '/images/community_extensions/social_preview/preview_community_extension_timestral.png'
layout: community_extension_doc
---
### Compatibility Matrix

This table covers all DuckDB releases that have not reached [end-of-life](https://duckdb.org/release_calendar), including LTS releases, and shows the 2 most recent patch releases of each. Older patch releases are not listed, but the extension may be available for them as well.

<div class="extension_compatibility_table"></div>

| Platform | 1.4.4 | 1.4.5 | 1.5.5 | 1.5.6 | 2.0.0 |
| :--- | :---: | :---: | :---: | :---: | :---: |
| Linux AMD64 | ❌ | ❌ | ❌ | ❌ | ✅ |
| Linux ARM64 | ❌ | ❌ | ❌ | ❌ | ✅ |
| macOS AMD64 | ❌ | ❌ | ❌ | ❌ | ✅ |
| macOS ARM64 | ❌ | ❌ | ❌ | ❌ | ✅ |
| Windows AMD64 | ❌ | ❌ | ❌ | ❌ | ✅ |
| Windows ARM64 | ❌ | ❌ | ❌ | ❌ | ❌ |
| WebAssembly | ❌ | ❌ | ❌ | ❌ | ❌ |

### Installing and Loading
```sql
INSTALL {{ page.extension.name }} FROM community;
LOAD {{ page.extension.name }};
```

{% if page.docs.hello_world %}
### Example
```sql
{{ page.docs.hello_world }}```
{% endif %}

{% if page.docs.extended_description %}
### About {{ page.extension.name }}
{{ page.docs.extended_description }}
{% endif %}
### Added Functions

<div class="extension_functions_table"></div>

|   function_name    | function_type | description | comment | examples |
|--------------------|---------------|-------------|---------|----------|
| timestral_forecast | aggregate     | NULL        | NULL    |          |
| timestral_models   | table         | NULL        | NULL    |          |
| timestral_version  | scalar        | NULL        | NULL    |          |

### Overloaded Functions

<div class="extension_functions_table"></div>

This extension does not add any function overloads.

### Added Types

<div class="extension_types_table"></div>

This extension does not add any types.

### Added Settings

<div class="extension_settings_table"></div>

|           name            |                                    description                                     | input_type | scope  | aliases |                typed_value                |
|---------------------------|------------------------------------------------------------------------------------|------------|--------|---------|-------------------------------------------|
| active_grammar_extensions | The grammar extensions used by the parser.                                         | VARCHAR[]  | LOCAL  | []      | [shell, timestral]                        |
| temp_directory            | Set the directory to which temporary files are written.                            | VARCHAR    | GLOBAL | []      | post.db.tmp                               |
| timestral_cache_dir       | Directory downloaded Timestral models are cached in                                | VARCHAR    | GLOBAL | []      | ~/.duckdb/timestral/v2                    |
| timestral_model           | Default Timestral model size: 5m, 28m, 123m or 209m                                | VARCHAR    | GLOBAL | []      | 5m                                        |
| timestral_model_path      | Explicit local .onnx file to use instead of the download cache (no checksum check) | VARCHAR    | GLOBAL | []      |                                           |
| timestral_model_url       | Base URL the Timestral .onnx files are downloaded from                             | VARCHAR    | GLOBAL | []      | https://assets.altertable.ai/timestral/v2 |