Month: 2026-04 — Nixtla/utilsforecast: Key features delivered include Pareto-Optimal Evaluation for Multi-Metric Model Selection, enabling Pareto-front based ranking across multiple metrics to guide model selection. Major bugs fixed: None reported this month. Overall impact and accomplishments: Enables data-driven, trade-off-aware model selection, reducing manual trial-and-error and enabling faster, better forecasting decisions; potential uplift in forecast performance by prioritizing well-balanced models. Technologies/skills demonstrated: Pareto-Optimal evaluation methodology, multi-criteria optimization, Python data science tooling, collaboration (co-authored by Saul).

February 2026 performance snapshot for Nixtla/statsforecast: Delivered a Simulation-based Sample Trajectory Generator for Time Series Forecasting, enabling the generation of multiple plausible future paths from fitted models to support scenario analysis and risk assessment. No major bugs fixed this month. Overall impact: enhanced forecasting analytics and decision support by enabling robust scenario testing across potential futures. Technologies demonstrated include Python-based time series modeling, simulation techniques, and disciplined change management as evidenced by targeted feature work and PR #1072.