oracle/accelerated-data-science
Jan 2026 – Mar 2026
3 Months active
Languages Used
Python
Technical Skills
Pythondata analysisdata sciencemachine learningmodel explainabilitytesting
Sumit Kumar Jangir
PROFILE
Sumit Kumar Jangir
Sumit Kumar Jangir enhanced the oracle/accelerated-data-science repository by developing and refining time-series forecasting features using Python and machine learning. He implemented Theta and ETS forecasters with automated seasonality detection, frequency normalization, and model explainability through SHAP integration for LGBM and XGBoost models. Sumit also improved anomaly detection testing and introduced dynamic, specification-driven report generation to increase configurability. His work included bug fixes for forecasting stability and the addition of Prophet-based single-series forecasting to boost performance. These contributions deepened the repository’s forecasting capabilities, improved interpretability for business users, and strengthened the reliability and maintainability of reporting pipelines.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
5
Lines of code
1,579
Activity Months3
Your Network
787 peopleSame Organization
@oracle.com769
Aaron YoungMember
Carlos-CA-AlvarezMember
Dyre TjeldvollMember
grungtaMember
Kaiyuan LiMember
KarthikMember
Liam R. HowlettMember
Liam R. HowlettMember
Martin BalinMember
Work History
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for oracle/accelerated-data-science focusing on forecasting enhancements, configuration normalization, and performance improvements to enable faster, more reliable business decisions.
3 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary for oracle/accelerated-data-science focusing on forecasting enhancements, configuration normalization, and performance improvements to enable faster, more reliable business decisions.
March 2026
February 2026
2 Commits • 1 Features
Feb 1, 2026Month 2026-02: Delivered targeted fixes and configurability enhancements in oracle/accelerated-data-science to improve forecast reliability and reporting accuracy. Key features delivered include a bug fix to AutoMLX and Theta forecaster for frequency normalization and forecast value handling, and a dynamic report title mechanism for Anomaly and Forecaster Operators driven by the specification. These changes reduce manual intervention, increase confidence in forecasts, and enable better business decisions through more accurate, configurable reports. Technologies demonstrated include Python-based forecasting components (AutoMLX, Theta), frequency normalization, spec-driven configuration, and Git-based collaboration. Overall impact: improved forecast stability, reduced risk in planning, and increased maintainability of reporting pipelines.
February 2026
2 Commits • 1 Features
Feb 1, 2026Month 2026-02: Delivered targeted fixes and configurability enhancements in oracle/accelerated-data-science to improve forecast reliability and reporting accuracy. Key features delivered include a bug fix to AutoMLX and Theta forecaster for frequency normalization and forecast value handling, and a dynamic report title mechanism for Anomaly and Forecaster Operators driven by the specification. These changes reduce manual intervention, increase confidence in forecasts, and enable better business decisions through more accurate, configurable reports. Technologies demonstrated include Python-based forecasting components (AutoMLX, Theta), frequency normalization, spec-driven configuration, and Git-based collaboration. Overall impact: improved forecast stability, reduced risk in planning, and increased maintainability of reporting pipelines.
January 2026
4 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for oracle/accelerated-data-science: Delivered major time-series forecasting enhancements, added model explainability, and strengthened testing to improve reliability and business value. Implemented Theta Forecaster with seasonal period detection, frequency normalization, and explanation reporting; introduced ETS Forecaster for traditional forecasting; integrated SHAP explainability for LGBM/XGBoost forecasting models; and expanded anomaly detection tests to ensure robust coverage. The changes enhance forecast accuracy, interpretability for business users, and testing rigor, enabling faster, more confident decision-making and more maintainable code.
4 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for oracle/accelerated-data-science: Delivered major time-series forecasting enhancements, added model explainability, and strengthened testing to improve reliability and business value. Implemented Theta Forecaster with seasonal period detection, frequency normalization, and explanation reporting; introduced ETS Forecaster for traditional forecasting; integrated SHAP explainability for LGBM/XGBoost forecasting models; and expanded anomaly detection tests to ensure robust coverage. The changes enhance forecast accuracy, interpretability for business users, and testing rigor, enabling faster, more confident decision-making and more maintainable code.
January 2026
Activity
Loading activity data...
Quality Metrics
Correctness84.4%
Maintainability82.2%
Architecture84.4%
Performance82.2%
AI Usage42.2%
Skills & Technologies
Programming Languages
Python
Technical Skills
PythonPython programmingdata analysisdata scienceforecastingmachine learningmodel explainabilityreport generationtestingtime series forecastingtime-series forecasting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline