Nixtla/utilsforecast
Jun 2025 – Jun 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
BacktestingData ProcessingSoftware DevelopmentTime Series Analysis
bartoshqr
PROFILE
Bartoshqr
Developed partial horizons backtesting support for the Nixtla/utilsforecast repository, enabling users to perform backtests even when full horizon data is unavailable. This feature was implemented by introducing an allow_partial_horizons parameter and refining the test_size calculation, allowing for greater flexibility in time series validation workflows. The core backtesting logic, including the _single_split and backtest_splits functions, was refactored and extended to accommodate partial horizons, with comprehensive tests added to ensure reliability. The work leveraged Python and Jupyter Notebook, focusing on backtesting, data processing, and time series analysis to streamline model validation and reduce data readiness constraints.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
39
Activity Months1
Your Network
4 peopleShared Repositories
4
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025Delivered Partial Horizons Backtesting Support in Nixtla/utilsforecast, enabling backtests when full horizon data is unavailable through a new allow_partial_horizons parameter and adjusted test_size calculation. Refactored and extended the backtesting core (including _single_split and backtest_splits) with comprehensive tests to ensure reliability with partial horizons. This work improves modeling flexibility and reduces data readiness friction, accelerating validation workflows for forecasting tasks.
1 Commits • 1 Features
Jun 1, 2025Delivered Partial Horizons Backtesting Support in Nixtla/utilsforecast, enabling backtests when full horizon data is unavailable through a new allow_partial_horizons parameter and adjusted test_size calculation. Refactored and extended the backtesting core (including _single_split and backtest_splits) with comprehensive tests to ensure reliability with partial horizons. This work improves modeling flexibility and reduces data readiness friction, accelerating validation workflows for forecasting tasks.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
BacktestingData ProcessingSoftware DevelopmentTime Series Analysis
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