idaholab/raven
Oct 2024 – Dec 2024
2 Months active
Languages Used
PythonCSV
Technical Skills
Code RefactoringData ScienceMachine LearningMerge Conflict ResolutionSoftware DevelopmentTime Series Analysis
Gabriel J. Soto
PROFILE
Gabriel J. Soto
Worked on the idaholab/raven repository to enhance synthetic time series data generation and validation, focusing on VARMA models with multiple targets. Developed features that integrated Fourier-based periodic signals and improved target-algorithm matching, enabling more accurate and realistic synthetic datasets for model training and evaluation. Refined data generation workflows by introducing helpers for ARMA-based synthetic data, burn-in periods, and parameter tuning, which streamlined test data creation and improved reliability. Addressed merge conflicts and ensured robust integration of new features. Utilized Python and CSV for data manipulation, signal processing, and testing, demonstrating depth in time series analysis and synthetic data engineering.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
3,954
Activity Months2
Your Network
66 peopleSame Organization
@inl.gov58
Bradley HuddlestonMember
BrettMember
Derek VranesMember
Butterworth, EvanMember
Jason M MillerMember
Joseph CoronaVazquezMember
Kevin HarwoodMember
Larry AagesenMember
Matthew StradlingMember
Shared Repositories
8
Work History
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 — Raven (idaholab/raven): Focused on enhancing synthetic VARMA time-series capabilities and test data reliability. Key features delivered include Fourier-based periodic signal integration into VARMA time series generation with new helpers and examples for ARMA-based synthetic data, and substantial refinement of VARMA interpolation data and test data generation (burn-in, regolding, and parameter tuning). These efforts strengthen model validation, improve realism of synthetic datasets, and accelerate development cycles. Technologies demonstrated include time-series modeling (VARMA), Fourier signal processing, test-data engineering, and Python-based tooling.
3 Commits • 2 Features
Dec 1, 2024December 2024 — Raven (idaholab/raven): Focused on enhancing synthetic VARMA time-series capabilities and test data reliability. Key features delivered include Fourier-based periodic signal integration into VARMA time series generation with new helpers and examples for ARMA-based synthetic data, and substantial refinement of VARMA interpolation data and test data generation (burn-in, regolding, and parameter tuning). These efforts strengthen model validation, improve realism of synthetic datasets, and accelerate development cycles. Technologies demonstrated include time-series modeling (VARMA), Fourier signal processing, test-data engineering, and Python-based tooling.
December 2024
October 2024
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10 | Consolidated delivery for idaholab/raven with a focus on SyntheticHistory reliability and accuracy. Delivered improvements to target-algorithm matching and trained-params handling to robustly associate interpolated features with their corresponding algorithms and targets, notably in VARMA scenarios with multiple targets. Resolved merge conflicts and added validation paths to ensure clean integration and stable synthetic history generation. This work enhances the quality of synthetic data used in model training and evaluation, reducing downstream rework.
October 2024
2 Commits • 1 Features
Oct 1, 2024Month: 2024-10 | Consolidated delivery for idaholab/raven with a focus on SyntheticHistory reliability and accuracy. Delivered improvements to target-algorithm matching and trained-params handling to robustly associate interpolated features with their corresponding algorithms and targets, notably in VARMA scenarios with multiple targets. Resolved merge conflicts and added validation paths to ensure clean integration and stable synthetic history generation. This work enhances the quality of synthetic data used in model training and evaluation, reducing downstream rework.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture76.0%
Performance68.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPython
Technical Skills
Code RefactoringData GenerationData ManipulationData ScienceMachine LearningMerge Conflict ResolutionSignal ProcessingSoftware DevelopmentSynthetic Data GenerationTestingTime Series Analysis
Repositories Contributed To
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