June 2026 (2026-06) monthly summary for Nixtla/statsforecast. This period focused on dependency hygiene and forward-compatibility enhancements to support modern Python runtimes. The primary delivery was a PyArrow upgrade that enables Python 3.14+ compatibility and removes previous version constraints, reducing friction for deployment across newer environments. No major defects were closed this month; the work emphasized maintainability and future-proofing rather than feature expansion.

Month: 2026-05; maintenance and security hardening focused on Nixtla/statsforecast. No new features; executed a targeted dependency upgrade to improve security and compatibility, ensuring reliability of forecasting workflows for downstream users.

April 2026 monthly summary for Nixtla/utilsforecast: Delivered clarity-focused documentation for loss function notation and completed a versioning system enhancement. No user-facing feature toggles; improved documentation quality and release reliability. Key deliverables included: Documentation Enhancement for Loss Function Notation Clarifications (commit 6f038bdac36637c216df9151cc2ab9655ebec104); Versioning System Enhancement: Switch to importlib.metadata and bump to 0.2.16 (commit ce2c7ddc7b71228ece21edf72ef9567d7467c0ab).

March 2026 delivered stability, security, and deployment flexibility across Nixtla repos. Key features include dependency stability and compatibility improvements in statsforecast, GitHub workflow permissions hardening, and making Spark an optional dependency in neuralforecast. These changes reduce runtime friction, lower security risk, and simplify installations for users not leveraging Spark, while maintaining full capabilities for environments that require it. Demonstrated strong dependency management, CI security, and modular release design across multiple repositories.

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.