arXiv Machine Learning August 4, 2026 By Seunghan Lee, Jaehoon Lee, Jun Seo, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn

Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting

Read the original on arXiv Machine Learning →

arXiv:2608. 01857v1 Announce Type: new Abstract: The direction of change --- whether a series will move up or down --- is often as important as its exact value in decisiondriven applications such as risk management and financial forecasting.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Post-Training Corrections for Improved Time-Series Forecasting

CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

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