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
Vijaye12 developed the TSPulse time-series forecasting model for the ibm-granite/granite-tsfm repository, consolidating classification and regression tasks into a unified classification workflow. Leveraging Python and PyTorch, they introduced patchwise reconstruction and dataset wrapping utilities, enhancing both anomaly detection and experimental flexibility. Their work included refactoring model architecture and configuration to streamline outputs and improve code organization, while also addressing static analysis and initialization issues to ensure code quality. By adding comprehensive unit tests in Jupyter Notebook, Vijaye12 improved reliability and regression prevention, ultimately reducing maintenance overhead and aligning the codebase with deployment and product-readiness goals.
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
70 people
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
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