sktime/sktime
Jan 2026 – Jan 2026
1 Month active
Languages Used
Python
Technical Skills
Python programmingdata analysispandasstatistical modelingtime series forecastingunit testing
Rohan Oruganti
PROFILE
Rohan Oruganti
Contributed to the sktime/sktime repository by enhancing the AutoTS forecasting module and improving reliability in time series analysis workflows. Addressed a bug in the ProximityForest _stdp function to ensure correct handling of NaN values, preventing calculation errors and improving data robustness. Developed new AutoTS features to support prediction intervals with multi-coverage and the integration of exogenous data, expanding the flexibility of forecasting models. Employed Python, pandas, and statistical modeling techniques to implement these changes, while also adding targeted unit tests to strengthen coverage and reduce regression risk. The work focused on practical improvements to forecasting accuracy and reliability.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
237
Activity Months1
Your Network
71 peopleSame Organization
@iisc.ac.in4
adityapguptaMember
Ghanshyam ChandraMember
Chitikela Veera Sai Charan TejaMember
Pandarasamy Arjunan (Samy)Member
Shared Repositories
67
Work History
January 2026
3 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for sktime/sktime focusing on reliability, forecasting enhancements, and testing. Key outcomes include a bug fix to NaN handling in ProximityForest _stdp, and significant AutoTS forecasting enhancements with prediction intervals and exogenous data support. These changes extend forecasting capabilities, improve reliability with NaN data, and strengthen test coverage to reduce regression risk.
3 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for sktime/sktime focusing on reliability, forecasting enhancements, and testing. Key outcomes include a bug fix to NaN handling in ProximityForest _stdp, and significant AutoTS forecasting enhancements with prediction intervals and exogenous data support. These changes extend forecasting capabilities, improve reliability with NaN data, and strengthen test coverage to reduce regression risk.
January 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture86.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Python programmingdata analysispandasstatistical modelingtime series forecastingunit testing
Repositories Contributed To
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