ibm-granite/granite-tsfm
May 2025 – May 2025
1 Month active
Languages Used
JinjaJupyter NotebookPython
Technical Skills
Anomaly DetectionCode OrganizationCode RefactoringDeep LearningFFT (Fast Fourier Transform)Hugging Face Transformers
vijaye12
PROFILE
Vijaye12
Developed and delivered the TSPulse time-series forecasting model for the ibm-granite/granite-tsfm repository, consolidating classification and regression tasks into a unified classification workflow. Enhanced the model’s anomaly detection capabilities by introducing patchwise reconstruction and dataset wrapping utilities, and updated configurations and outputs to support the new structure. Addressed static analysis and initialization issues by refactoring code and ensuring proper mask token handling, improving code quality and maintainability. Implemented comprehensive unit tests to ensure reliability and prevent regressions. The work leveraged Python, PyTorch, and Hugging Face Transformers, resulting in improved forecasting readiness and streamlined experimentation for time-series analysis tasks.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
7,341
Activity Months1
Your Network
77 peopleShared Repositories
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Arindam JatiMember
Arindam JatiMember
Thomas BohnstinglMember
ChernovAndreyMember
Ferran Aran DomingoMember
Lars GrafMember
Michel de RuiterMember
NataliaMember
Stuart SiegelMember
Work History
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ibm-granite/granite-tsfm: Delivered a new time-series forecasting model (TSPulse) and consolidated classification/regression tasks into a single classification workflow, with patchwise reconstruction and dataset wrapping utilities. Updated configurations and outputs to reflect the new structure and enhanced anomaly-detection capabilities, enabling more reliable forecasting and streamlined experimentation.
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for ibm-granite/granite-tsfm: Delivered a new time-series forecasting model (TSPulse) and consolidated classification/regression tasks into a single classification workflow, with patchwise reconstruction and dataset wrapping utilities. Updated configurations and outputs to reflect the new structure and enhanced anomaly-detection capabilities, enabling more reliable forecasting and streamlined experimentation.
May 2025
Activity
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Quality Metrics
Correctness83.4%
Maintainability83.4%
Architecture83.4%
Performance73.4%
AI Usage26.6%
Skills & Technologies
Programming Languages
JinjaJupyter NotebookPython
Technical Skills
Anomaly DetectionCode OrganizationCode RefactoringDeep LearningFFT (Fast Fourier Transform)Hugging Face TransformersMachine LearningModel Architecture DesignModel ConfigurationModel RefactoringPyTorchTime Series AnalysisTransformer ModelsUnit Testing
Repositories Contributed To
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