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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 4·5 min read
Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting
TL;DR
CosDir is a novel direction-aware loss function for time series forecasting that enhances directional accuracy without sacrificing magnitude. It outperforms traditional loss functions, particularly in predicting small changes.
✦ Why It Matters
Implement CosDir in your forecasting models to enhance directional accuracy in predictions today.
Key Takeaways
- Integrate CosDir for improved directional forecasting accuracy.
- Use CosDir-UW to automate hyperparameter tuning for different datasets.
- Conduct extensive experiments to validate model performance with CosDir.
- Focus on small directional changes in time series data for better predictions.
How It Works
CosDir aligns the prediction and target difference vectors using cosine similarity, which emphasizes the direction of change rather than just the magnitude. This approach ensures that even small directional shifts are effectively learned, addressing the shortcomings of MSE.