TSAuditor: An automated framework for time-series data auditing
Prevent model failure by catching hidden data leakage and chronological errors in your time-series pipelines.
30-Second TL;DR
What Changed
Automated detection of chronological breaks and data leakage
Why It Matters
By automating the detection of subtle data quality issues, this tool helps prevent model performance degradation caused by faulty time-series features or broken sequences.
What To Do Next
Integrate tsauditor into your data pipeline to validate chronological consistency before feeding time-series data into your training models.
Key Points
- •Automated detection of chronological breaks and data leakage
- •Identifies sudden sequential spikes in global boundaries
- •Provides evidence-based descriptions and suggested data fixes
- •Lightweight and available via PyPI for easy integration
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