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moirai

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crypto-trading-backtesting

This project was driven by the need to provide our client Mela, a startup focusing on simplifying cryptocurrency investments, with a reliable and scalable platform for backtesting trading strategies. Additionally, leveraging advanced forecasting methods would enhance the platform's capability to predict future price trends with greater accuracy.

  • Updated Jun 27, 2024
  • Jupyter Notebook

Self-hosted forecasting + prediction service. Five zero-shot time-series foundation models (Chronos-2, TimesFM 2.5, Moirai-2, Toto-1, Sundial) across six forecast types, plus nine supervised tabular ML backends (LightGBM, XGBoost, sklearn family) with calibrated / stacking / diversified meta-learners. Unified REST API + MCP server.

  • Updated Oct 19, 2026
  • Python

Do zero-shot time-series foundation models beat simple baselines for battery SOH prediction? A reproducible three-dataset benchmark (MIT-Stanford / NASA PCoE / CALCE UMD) with paired bootstrap CIs, Wilcoxon tests, multi-seed variance and cross-dataset transfer.

  • Updated Oct 23, 2026
  • Python

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