February 2026 focused on strengthening data integrity and security across Nixtla repos while maintaining a lean, scalable dependency surface. Delivered a serialization reliability upgrade and hardened security posture through timely dependency updates, laying groundwork for safer, more maintainable code.

January 2026 monthly summary across Nixtla/statsforecast, Nixtla/neuralforecast, and Nixtla/utilsforecast. Focused on delivering cross-platform compatibility improvements, documentation pipeline updates, and distributed evaluation readiness, with unpinned core dependencies to enable latest versions and stabilize CI.

December 2025 monthly summary focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated across the Nixtla forecasting suite.

Monthly summary for Nixtla/utilsforecast - 2025-11: Focused on elevating documentation quality and reliability by integrating Mkdocstrings into the docs generation process and stabilizing the docs pipeline. This work supports faster onboarding, better API discoverability, and more maintainable documentation workflows for the utilsforecast project.

October 2025 monthly summary for Nixtla repositories focusing on delivering stability, onboarding improvements, and API enhancements across statsforecast and utilsforecast.

September 2025 monthly summary for Nixtla/utilsforecast focused on stabilizing the CI/CD test suite in headless environments and delivering reliable test outcomes. A targeted fix was implemented to force the Matplotlib backend to Agg in the pytest workflow, addressing CI flakiness and ensuring consistent test results across headless runners.

July 2025 monthly summary focused on delivering in-sample prediction enhancements with static feature support in neuralforecast. This update enables use of static features by passing static and static_cols to the TimeSeriesDataset during in-sample predictions, improving model calibration and feature utilization for static data.