ibm-granite/granite-tsfm
May 2025 – Oct 2025
5 Months active
Languages Used
PythonSQLTorchJSONJupyter Notebook
Technical Skills
Code RefactoringConformal PredictionData ScienceMachine LearningModel UpdatingProbabilistic Forecasting
Natalia
PROFILE
Natalia
Natalia developed and enhanced time-series forecasting and anomaly detection capabilities in the ibm-granite/granite-tsfm repository, focusing on conformal prediction frameworks and probabilistic post-processing. She implemented adaptive weighting, horizon-aware aggregation, and online model updates using Python, PyTorch, and Jupyter Notebooks, improving calibration accuracy and real-time adaptability. Her work included robust handling of edge cases, CUDA support, and notebook-based workflows for reproducible experimentation. By refining data preprocessing, outlier detection, and statistical modeling, Natalia addressed both reliability and maintainability, delivering production-ready solutions for monitoring and alerting. She also fixed critical bugs, ensuring stable forecasting and reducing risks in edge scenarios.
Overall Statistics
Feature vs Bugs
64%Features
Repository Contributions
40Total
Bugs
5
Commits
40
Features
9
Lines of code
7,579
Activity Months5
Your Network
12 people
Work History
October 2025
2 Commits
Oct 1, 2025Month: 2025-10 — ibm-granite/granite-tsfm: stability and maintainability focus in forecasting module. Key features delivered: none this month. Major bugs fixed: Forecast Horizon Alignment Bug fixed by looping up to min(H, N) to prevent index errors when N < H; minor style spacing adjustment inside the same loop for readability. Overall impact: increased robustness and reliability of forecasting calculations, reduced risk of incorrect outputs in edge cases, and improved code readability. Technologies/skills demonstrated: TypeScript, algorithm boundary handling, code readability improvements, commit hygiene, and bug-fix discipline.
2 Commits
Oct 1, 2025Month: 2025-10 — ibm-granite/granite-tsfm: stability and maintainability focus in forecasting module. Key features delivered: none this month. Major bugs fixed: Forecast Horizon Alignment Bug fixed by looping up to min(H, N) to prevent index errors when N < H; minor style spacing adjustment inside the same loop for readability. Overall impact: increased robustness and reliability of forecasting calculations, reduced risk of incorrect outputs in edge cases, and improved code readability. Technologies/skills demonstrated: TypeScript, algorithm boundary handling, code readability improvements, commit hygiene, and bug-fix discipline.
October 2025
September 2025
9 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for ibm-granite/granite-tsfm: Delivered targeted improvements to probabilistic post-processors and conformal prediction, enhancing calibration stability, outlier handling, and performance for time-series forecasting. Achieved robust data handling, CUDA-enabled weighting, and notebook quality improvements, delivering tangible business value through more reliable uncertainty estimates and faster experimentation.
September 2025
9 Commits • 2 Features
Sep 1, 2025September 2025 monthly summary for ibm-granite/granite-tsfm: Delivered targeted improvements to probabilistic post-processors and conformal prediction, enhancing calibration stability, outlier handling, and performance for time-series forecasting. Achieved robust data handling, CUDA-enabled weighting, and notebook quality improvements, delivering tangible business value through more reliable uncertainty estimates and faster experimentation.
July 2025
5 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for ibm-granite/granite-tsfm: Delivered a reproducible Time-Series Transformer (TTM) conformal anomaly detection workflow and notebook-based demonstration on the TSB-UAD dataset, with integrated anomaly visualization. Consolidated five commits into the feature set, focusing on notebook-based experimentation, data preparation calibration, and forecast generation refinements. Enhanced the conformal prediction module with calibrated outlier scoring and improved interpretation of signed errors. Performed notebook hygiene improvements (cleanup, execution reset, and execution-order adjustments) to ensure reliability and smoother onboarding. Overall, this work strengthens proactive anomaly detection capabilities, shortens iteration cycles for data scientists, and provides a solid, production-ready foundation for monitoring and alerting.
5 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for ibm-granite/granite-tsfm: Delivered a reproducible Time-Series Transformer (TTM) conformal anomaly detection workflow and notebook-based demonstration on the TSB-UAD dataset, with integrated anomaly visualization. Consolidated five commits into the feature set, focusing on notebook-based experimentation, data preparation calibration, and forecast generation refinements. Enhanced the conformal prediction module with calibrated outlier scoring and improved interpretation of signed errors. Performed notebook hygiene improvements (cleanup, execution reset, and execution-order adjustments) to ensure reliability and smoother onboarding. Overall, this work strengthens proactive anomaly detection capabilities, shortens iteration cycles for data scientists, and provides a solid, production-ready foundation for monitoring and alerting.
July 2025
June 2025
11 Commits • 3 Features
Jun 1, 2025June 2025 summary for ibm-granite/granite-tsfm: Delivered horizon-aware improvements to probabilistic scoring and conformal predictions, enhancing accuracy and configurability across forecast horizons. Implemented horizon-aligned aggregation for PostHocProbabilisticProcessor, introduced adaptive weighting in the conformal toolkit, and preserved temporal characteristics in probabilistic scoring to prevent time-smearing. Expanded testing coverage and refreshed documentation to support production reliability and easier validation. These changes strengthen anomaly detection quality, provide flexible aggregation strategies, and solidify the core conformal logic for scalable deployment.
June 2025
11 Commits • 3 Features
Jun 1, 2025June 2025 summary for ibm-granite/granite-tsfm: Delivered horizon-aware improvements to probabilistic scoring and conformal predictions, enhancing accuracy and configurability across forecast horizons. Implemented horizon-aligned aggregation for PostHocProbabilisticProcessor, introduced adaptive weighting in the conformal toolkit, and preserved temporal characteristics in probabilistic scoring to prevent time-smearing. Expanded testing coverage and refreshed documentation to support production reliability and easier validation. These changes strengthen anomaly detection quality, provide flexible aggregation strategies, and solidify the core conformal logic for scalable deployment.
May 2025
13 Commits • 3 Features
May 1, 2025May 2025: Delivered configurability and accuracy improvements to the granite-tsfm conformal prediction framework, added Gaussian probabilistic methods support, and enabled online updates with advanced metrics for PostHocProbabilisticProcessor. Focused on API clarity, stability, and test coverage to drive business value through higher-confidence predictive calibration and real-time adaptability.
13 Commits • 3 Features
May 1, 2025May 2025: Delivered configurability and accuracy improvements to the granite-tsfm conformal prediction framework, added Gaussian probabilistic methods support, and enabled online updates with advanced metrics for PostHocProbabilisticProcessor. Focused on API clarity, stability, and test coverage to drive business value through higher-confidence predictive calibration and real-time adaptability.
May 2025
Activity
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Quality Metrics
Correctness87.2%
Maintainability87.0%
Architecture84.4%
Performance75.8%
AI Usage22.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPythonSQLTorch
Technical Skills
Algorithm DevelopmentAnomaly DetectionCode FormattingCode RefactoringConformal PredictionData AggregationData PreprocessingData ProcessingData ScienceDebuggingDeep LearningDocumentationJupyter NotebooksMachine LearningMachine Learning Pipelines
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