ibm-granite/granite-tsfm
May 2025 – Jun 2025
2 Months active
Languages Used
Jupyter NotebookPythonBashCSVMarkdownSQLTOML
Technical Skills
Anomaly DetectionCode RefactoringData PreprocessingData ScienceDocumentationJupyter Notebooks
sumanm03
PROFILE
Sumanm03
Suman Mondal developed and refined end-to-end time series anomaly detection capabilities for the ibm-granite/granite-tsfm repository, focusing on the TSPulse model. He architected a robust detection pipeline in Python, leveraging PyTorch and Scikit-learn, and delivered a dedicated Jupyter notebook to demonstrate example data, score computation, and boundary adjustment. Suman improved maintainability by refactoring scaling logic and consolidating utilities, while enhancing configuration, plotting, and scoring workflows for faster iteration. He addressed critical bugs affecting pipeline sequencing and reliability, expanded scoring mechanisms, and introduced full-batch evaluation and reproducibility tooling, resulting in a more stable, testable, and developer-friendly codebase.
Overall Statistics
Feature vs Bugs
59%Features
Repository Contributions
38Total
Bugs
9
Commits
38
Features
13
Lines of code
9,929
Activity Months2
Your Network
70 people
Work History
June 2025
30 Commits • 12 Features
Jun 1, 2025June 2025 monthly summary for the ibm-granite/granite-tsfm repository. Delivered critical bug fixes that stabilize the data processing pipeline, expanded scoring capabilities to support richer evaluation scenarios, and introduced practical features that accelerate experimentation and onboarding. Key outcomes include corrected pipeline sequencing, broadened score mechanisms, full-batch model evaluation, API consistency improvements, and lightweight configuration for a tspulse dummy model. The work is underpinned by improved test coverage and documentation, enhancing reliability, maintainability, and developer productivity.
30 Commits • 12 Features
Jun 1, 2025June 2025 monthly summary for the ibm-granite/granite-tsfm repository. Delivered critical bug fixes that stabilize the data processing pipeline, expanded scoring capabilities to support richer evaluation scenarios, and introduced practical features that accelerate experimentation and onboarding. Key outcomes include corrected pipeline sequencing, broadened score mechanisms, full-batch model evaluation, API consistency improvements, and lightweight configuration for a tspulse dummy model. The work is underpinned by improved test coverage and documentation, enhancing reliability, maintainability, and developer productivity.
June 2025
May 2025
8 Commits • 1 Features
May 1, 2025May 2025: Delivered end-to-end Time Series Anomaly Detection for the TSPulse model in granite-tsfm, including a dedicated notebook with example data, score computation, boundary adjustment, and a robust detection pipeline (scaling utilities, postprocessing, type handling, parameter handling, plotting, and scoring). Refactored architecture to improve maintainability: moved scaling logic into tspulse utilities and consolidated utilities into utility/classes; updated docstrings. Fixed critical reliability bugs: ensured smoothing window is correctly passed to the post processor and resolved a mode-handling bug, resulting in more stable anomaly scores. Enhanced configuration, plotting, and scoring workflows, enabling faster iteration and clearer operational insights for proactive monitoring.
May 2025
8 Commits • 1 Features
May 1, 2025May 2025: Delivered end-to-end Time Series Anomaly Detection for the TSPulse model in granite-tsfm, including a dedicated notebook with example data, score computation, boundary adjustment, and a robust detection pipeline (scaling utilities, postprocessing, type handling, parameter handling, plotting, and scoring). Refactored architecture to improve maintainability: moved scaling logic into tspulse utilities and consolidated utilities into utility/classes; updated docstrings. Fixed critical reliability bugs: ensured smoothing window is correctly passed to the post processor and resolved a mode-handling bug, resulting in more stable anomaly scores. Enhanced configuration, plotting, and scoring workflows, enabling faster iteration and clearer operational insights for proactive monitoring.
Activity
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Quality Metrics
Correctness82.4%
Maintainability85.4%
Architecture80.4%
Performance70.0%
AI Usage22.2%
Skills & Technologies
Programming Languages
BashCSVJupyter NotebookMarkdownPythonSQLTOML
Technical Skills
API RefactoringAnomaly DetectionCode MaintenanceCode QualityCode RefactoringCommand-line InterfaceData AnalysisData ManagementData PreprocessingData ProcessingData ScienceData VisualizationDependency ManagementDocumentationEnvironment Setup
Repositories Contributed To
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