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Arindam Jati

PROFILE

Arindam Jati

Arindam Jati enhanced the ibm-granite/granite-tsfm repository by developing and refining data workflows, improving notebook reliability, and streamlining model integration. Over two months, Arindam consolidated onboarding materials, aligned API usage with updated function signatures, and standardized code formatting for clarity and maintainability. Using Python and Jupyter Notebooks, Arindam restructured data handling logic, integrated source code for gift data, and improved reproducibility through deterministic seeding. The work included bug fixes in model output handling and expanded documentation with docstrings and README updates. These contributions strengthened code quality, reduced onboarding friction, and ensured more reliable time series forecasting and deep learning workflows.

Overall Statistics

Feature vs Bugs

64%Features

Repository Contributions

25Total
Bugs
4
Commits
25
Features
7
Lines of code
8,163
Activity Months2

Work History

March 2025

3 Commits • 1 Features

Mar 1, 2025

March 2025: Delivered the TTM Getting Started Notebook feature for granite-tsfm by consolidating three commits into a cohesive update that aligns API usage with the latest release and get_model function signatures, standardizes keyword argument formatting for improved readability, and updates the get_model() documentation link to the function docstring for clearer guidance. This work reduces onboarding friction and simplifies future maintenance.

February 2025

22 Commits • 6 Features

Feb 1, 2025

February 2025 performance summary for ibm-granite/granite-tsfm: Delivered data workflow improvements and notebook reliability while boosting code quality and documentation. Gift data processing was streamlined with source code integration and frequency map restructuring; model outputs are more reliable due to fixes in get_model/get_gift_model; notebook experiences improved with argparse compatibility and results exposure; and overall maintainability strengthened by Ruff linting, docstrings, and README updates. Deterministic seeding improvements further enhance experiment reproducibility.

Activity

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Quality Metrics

Correctness88.0%
Maintainability88.0%
Architecture87.2%
Performance82.4%
AI Usage20.0%

Skills & Technologies

Programming Languages

CSVJupyter NotebookMarkdownPython

Technical Skills

Code CleanupCode FormattingCode RefactoringCodebase MaintenanceConfiguration ManagementData AnalysisData HandlingData PreprocessingData ScienceDeep LearningDocumentationError HandlingGitGluonTSJupyter Notebooks

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

ibm-granite/granite-tsfm

Feb 2025 Mar 2025
2 Months active

Languages Used

CSVJupyter NotebookMarkdownPython

Technical Skills

Code CleanupCode FormattingCode RefactoringCodebase MaintenanceConfiguration ManagementData Analysis

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