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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
Multivariate Time Series Forecasting needs Cross Variable Loss
TL;DR
Cross-Variable Loss (CvLoss) is introduced to enhance multivariate time series forecasting by addressing the overlooked dependencies among future variables. This method improves forecasting accuracy by penalizing inconsistencies in forecast residuals across variable interactions.
✦ Why It Matters
Implement CvLoss in your forecasting models to improve accuracy and consistency in multivariate time series predictions.
Key Takeaways
- Adopt Cross-Variable Loss to enhance forecasting accuracy.
- Evaluate dependencies among future variables in your models.
- Integrate CvLoss with existing forecasting frameworks.
- Focus on reducing inconsistencies in forecast residuals.
How It Works
CvLoss operates by analyzing the relationships between different variables in a multivariate time series. It constructs a cross-variable graph that captures these relationships and penalizes forecast residuals that deviate from expected patterns.
By doing so, it encourages models to maintain consistency across variable interactions, whether they occur simultaneously or with delays.