google-research/timesfm
Feb 2025 – Mar 2025
2 Months active
Languages Used
MarkdownPythonTOML
Technical Skills
Data ScienceDeep LearningDistributed ComputingJupyter NotebookMachine LearningPyTorch
misha-chertushkin
PROFILE
Misha-chertushkin
Contributed to the google-research/timesfm repository by developing scalable fine-tuning infrastructure and enhancing dataset support for time-series modeling. Leveraged Python and PyTorch to implement multi-GPU training, refactored quantile functions for clarity, and improved data tooling to support diverse frequency types and stock data preparation. Updated Jupyter Notebooks and documentation to streamline onboarding and clarify PyTorch-based workflows. Introduced a dedicated finetuning folder to standardize model training, and managed package versioning with TOML for reliable deployment. Focused on accelerating experimentation, improving maintainability, and enabling faster research-to-production cycles through robust version control, distributed computing, and clear release management practices.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
8Total
Bugs
1
Commits
8
Features
4
Lines of code
1,854
Activity Months2
Your Network
4715 peopleSame Organization
@google.com4703
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
March 2025
2 Commits • 2 Features
Mar 1, 2025March 2025: Delivered new finetuning infrastructure to accelerate experimentation and training efficiency; released packaging changes with version 1.2.9, enabling dependable deployment. No major bugs fixed this month; focus on feature delivery and release hygiene. Result: improved training flexibility, clearer release cadence, and faster iteration cycles for research-to-production.
2 Commits • 2 Features
Mar 1, 2025March 2025: Delivered new finetuning infrastructure to accelerate experimentation and training efficiency; released packaging changes with version 1.2.9, enabling dependable deployment. No major bugs fixed this month; focus on feature delivery and release hygiene. Result: improved training flexibility, clearer release cadence, and faster iteration cycles for research-to-production.
March 2025
February 2025
6 Commits • 2 Features
Feb 1, 2025February 2025 performance summary for google-research/timesfm focused on delivering scalable fine-tuning capabilities, improving data tooling, and stabilizing notebooks. Key features delivered include TimesFM Finetuning and Dataset Enhancements with multi-GPU support, a practical finetuning example, and dataset enhancements (frequency type support, stock data fetch/prepare) with updated README documenting PyTorch finetuning and multi-GPU usage. Completed Quantile Function Refactor to improve clarity and consistency of quantile creation across the project. Fixed a notebook reliability issue by correcting import paths for FinetuningConfig and TimesFMFinetuner in FinetuningNotebook. These changes accelerate large-scale training, broaden data support for stock time-series, improve maintainability, and reduce onboarding friction. Demonstrated technologies and skills include PyTorch-based finetuning, multi-GPU orchestration, dataset preprocessing, Python code refactoring, naming standardization, and documentation/PR feedback iteration.
February 2025
6 Commits • 2 Features
Feb 1, 2025February 2025 performance summary for google-research/timesfm focused on delivering scalable fine-tuning capabilities, improving data tooling, and stabilizing notebooks. Key features delivered include TimesFM Finetuning and Dataset Enhancements with multi-GPU support, a practical finetuning example, and dataset enhancements (frequency type support, stock data fetch/prepare) with updated README documenting PyTorch finetuning and multi-GPU usage. Completed Quantile Function Refactor to improve clarity and consistency of quantile creation across the project. Fixed a notebook reliability issue by correcting import paths for FinetuningConfig and TimesFMFinetuner in FinetuningNotebook. These changes accelerate large-scale training, broaden data support for stock time-series, improve maintainability, and reduce onboarding friction. Demonstrated technologies and skills include PyTorch-based finetuning, multi-GPU orchestration, dataset preprocessing, Python code refactoring, naming standardization, and documentation/PR feedback iteration.
Activity
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Quality Metrics
Correctness95.0%
Maintainability95.0%
Architecture95.0%
Performance92.6%
AI Usage30.0%
Skills & Technologies
Programming Languages
MarkdownPythonTOML
Technical Skills
Data ScienceDeep LearningDistributed ComputingJupyter NotebookMachine LearningPyTorchPythonPytorchdata analysisdata sciencedocumentationmachine learningmodel trainingmulti-GPU trainingpackage management
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