timestral

Run Mistral's Timestral time-series foundation models inside DuckDB

Mistral Timestral: Tiny 209M AI Predicts Stocks Locally on CPU! Zero-shot probabilistic forecasting foundation model.

Independent Windows app for battery life and charging forecasts, powered by local Timestral AI.

Testing AI forecasting models against reality. Reproducible benchmarks, real world datasets and honest results.

Timestral forecasting for Apache DataFusion

Postgres extension for time-series forecasting with Chronos-2 and Timestral.

macOS menu bar monitor for your Mac's vital signs, with on-device forecasts by Mistral Timestral

Native .NET inference for the Kronos K-line, TimesFM 3.0, t0 and Timestral foundation model times series forecasting.

Time Series Peakbench: benchmarking load forecasters for battery peak shaving

Rust/Burn port of Mistral's Timestral-5M forecaster, native and in the browser (WebAssembly, WebGPU).

Pure-Zig train -> quantize -> deploy framework for ternary {-1,0,+1} MCU models

Findings and training setup for Mistral's Timestral time-series model: fine-tuning script (Mistral ships none), eval harness, and results. Benchmarked on Kalshi BTC 15-min markets.

AI weather in your browser: Mistral's Timestral model forecasts the next 24 hours, running entirely client-side (ONNX Runtime Web).

Zero-shot sales forecasting with Mistral AI's Timestral on Databricks: Unity Catalog, MLflow, serverless GPU, Prophet baseline

Live Dashboard for Uncertainty Quantification Benchmarking with Timestral

Run, compare and inspect time-series foundation models (Timestral, Chronos-2, TimesFM, t0-gamma, TiRex-2) on your own machine. Unofficial, local-first.

Your Local AI Hedge Fund Manager.

Local time-series forecasting with Mistral Timestral, reproducible backtests, and an MCP interface.

Grafana plugin for probabilistic time series forecasting with Mistral's Timestral model: forecast panel, Explore and panel-menu actions, and an alerting-ready data source.

Reproducible benchmark of Timestral-209M on public M4 monthly series