February 2026 monthly summary focused on delivering forecasting evaluation improvements and associated code ownership across the sktime repository. Key outcomes: - Delivered Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE) metrics for forecasting tasks, enabling more accurate evaluation for targets with exponential growth. - Implemented MSLE calculation and integration points within the forecasting metric suite, including a dedicated MeanSquaredLogError class and supporting function with numerical stability via log1p. - Exposed the new metrics through the forecasting API, ensuring compatibility with the existing metric architecture and workflow. Impact and business value: - Improves model selection and reliability for forecasting tasks in domains with large target value ranges (e.g., sales, demand planning), leading to better resource planning and revenue forecasting. - Provides a robust, numerically stable metric framework that reduces errors due to scale, aiding more informed decision-making. Technologies/skills demonstrated: - Python, NumPy (log1p for numerical stability), object-oriented design, and integration with a complex metric architecture. - Clear commit/PR traceability and API exposure, aligning with code quality and documentation standards. Reference: - Commit: 3682dc5be0b439e1c0b7ada0b90cb4ebafdc2356; PRs #9139, #9140 (MSLE/RMSLE implementation)

Concise monthly summary for 2025-12 focused on enhancing macOS ARM64 support in the Jenkins Infra plugin-modernizer-tool to reduce provisioning errors and improve reliability for JDK discovery and usage.

November 2025 highlights: Strengthened forecast evaluation and documentation in sktime/sktime by delivering two major contributions. Key features delivered: (1) NaiveForecaster documentation enhancement: seasonal strategy example added to the docstring (sp=12) to aid user guidance; (2) MAAPE metric: implemented mean_arctangent_absolute_percentage_error, added wrapper class, API exposure, and test scaffolding to improve robustness for intermittent demand. Major bugs fixed: none reported; efforts focused on quality-of-life improvements and analytical capabilities. Overall impact: clearer docs and a more robust, zero-value-tolerant metric improving business value by reducing user confusion and enabling more reliable model assessments. Technologies/skills demonstrated: Python, metric design, API/SDK parity with existing sktime patterns, doc contributions, testing.