Revolutionizing Time-Series Forecasting: Adaptive Loss Functions Pave the Way

research #time series 📝 Blog|Analyzed: Feb 12, 2026 05:00•

Published: Feb 12, 2026 02:45

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1 min read

Analysis

Lin Zhuchen's team at Peking University is making waves in time-series forecasting! Their research introduces an innovative approach to improve multi-step time series predictions by adapting the loss function itself, potentially leading to more accurate and reliable forecasts across various applications.

Key Takeaways

•The research addresses the limitations of Mean Squared Error (MSE) in time-series forecasting.
•The new method, Quadratic Direct Forecast (QDF), treats the loss function as a learnable object.
•QDF aims to enhance long-term forecasting accuracy without changing model architecture.

Reference / Citation

View Original

"By restructuring the weighted structure of the training objective, the research introduces explicit modeling of the correlation and uncertainty differences of prediction steps."

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雷锋网Feb 12, 2026 02:45

* Cited for critical analysis under Article 32.

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