Nixtla/utilsforecast
Nov 2024 – Feb 2025
2 Months active
Languages Used
PythonYAML
Technical Skills
Data AnalysisForecastingPythonSoftware DevelopmentTime Series AnalysisLoss Functions
Marco Zanotti
PROFILE
Marco Zanotti
Contributed to the Nixtla/utilsforecast repository by developing advanced forecast evaluation metrics, focusing on robust model assessment against seasonal naive baselines. Over two months, implemented MSSE and RMSSE metrics with comprehensive Python unit tests and documentation, enhancing the toolkit for time series analysis and model selection. Later, introduced Scaled Quantile Loss (SQL) and Scaled Multi-Quantile Loss (SMQL), normalizing quantile losses by the MAE of the seasonal naive model to enable fairer cross-series comparisons. Refactored loss computations into a reusable helper function, improving code maintainability and extensibility. Demonstrated expertise in Python, metric design, data analysis, and software development practices.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,310
Activity Months2
Your Network
4 peopleShared Repositories
4
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a robust forecast-evaluation enhancement in Nixtla/utilsforecast by introducing Scaled Quantile Loss (SQL) and Scaled Multi-Quantile Loss (SMQL). These metrics normalize quantile losses by the MAE of the seasonal naive baseline, enabling fairer cross-series comparisons. As part of this work, existing loss computations were refactored into a reusable _scale_loss helper to improve code organization, reduce duplication, and accelerate future metric additions. The changes were implemented in the commit 5aeb00e910b206abbe0e7c33d1d5274b2e8f064b.
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered a robust forecast-evaluation enhancement in Nixtla/utilsforecast by introducing Scaled Quantile Loss (SQL) and Scaled Multi-Quantile Loss (SMQL). These metrics normalize quantile losses by the MAE of the seasonal naive baseline, enabling fairer cross-series comparisons. As part of this work, existing loss computations were refactored into a reusable _scale_loss helper to improve code organization, reduce duplication, and accelerate future metric additions. The changes were implemented in the commit 5aeb00e910b206abbe0e7c33d1d5274b2e8f064b.
February 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key feature delivered: Added MSSE and RMSSE metrics to the utilsforecast library with Python implementations, tests, and documentation updates. This enhancement enables robust evaluation of forecasting models against a seasonal naive baseline, driving better model selection and forecasting accuracy for end users. No major bugs reported this month; the focus was on feature delivery, test coverage, and documenting usage for the team and external users. Overall impact: strengthens the evaluation toolkit, improves model quality decisions, and reduces forecasting risk for business planning. Technologies/skills demonstrated: Python, unit testing, documentation, metric design, and baseline comparison integration.
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key feature delivered: Added MSSE and RMSSE metrics to the utilsforecast library with Python implementations, tests, and documentation updates. This enhancement enables robust evaluation of forecasting models against a seasonal naive baseline, driving better model selection and forecasting accuracy for end users. No major bugs reported this month; the focus was on feature delivery, test coverage, and documenting usage for the team and external users. Overall impact: strengthens the evaluation toolkit, improves model quality decisions, and reduces forecasting risk for business planning. Technologies/skills demonstrated: Python, unit testing, documentation, metric design, and baseline comparison integration.
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Data AnalysisForecastingLoss FunctionsPandasPolarsPythonSoftware DevelopmentTime Series Analysis
Repositories Contributed To
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