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New loss functions improve time-series forecasting accuracy · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers propose novel loss functions for time-series forecasting models to improve accuracy and efficiency. The first paper introduces Time-o1, which transforms label sequences into decorrelated components to mitigate autocorrelation and reduce the number of optimization tasks. The second paper presents DistDF, which uses a joint-distribution Wasserstein alignment to address biases in standard forecasting methods caused by label autocorrelation. Both methods claim to achieve state-of-the-art performance and are compatible with various forecasting models. AI

IMPACT These new loss functions could lead to more accurate and efficient time-series forecasting models, benefiting applications in finance, weather prediction, and demand forecasting.

RANK_REASON Two academic papers published on arXiv proposing new methods for time-series forecasting.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New loss functions improve time-series forecasting accuracy · 2 sources tracked

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Two academic papers published on arXiv proposing new methods for time-series forecasting.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hao Wang, Licheng Pan, Zhichao Chen, Xu Chen, Qingyang Dai, Lei Wang, Haoxuan Li, Zhouchen Lin ·

    Time-o1: Time-Series Forecasting Needs Transformed Label Alignment

    arXiv:2505.17847v3 Announce Type: replace-cross Abstract: Training time-series forecasting models poses unique challenges in loss function design. Most existing approaches adopt temporal mean squared error, but this study reveals two critical limitations: (1) it ignores the prese…

  2. arXiv cs.AI TIER_1 Deutsch(DE) · Hao Wang, Licheng Pan, Yuan Lu, Zhixuan Chu, Xiaoxi Li, Shuting He, Zhichao Chen, Haoxuan Li, Qingsong Wen, Zhouchen Lin ·

    DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment

    arXiv:2510.24574v3 Announce Type: replace-cross Abstract: Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approach resorts to minimizing the conditional ne…

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