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English(EN) TimeES: Probabilistic and Deterministic Time Series Forecasting via Evolutionary Spectra

新研究应对长期时间序列预测挑战 · 跟踪 6 个来源

作者 PulseAugur 编辑部 · [7 个来源] ·

arXiv 上发表的多篇研究论文介绍了长期时间序列预测的新方法，解决了非平稳性和时间分布变化等挑战。TStruct 专注于学习共享的时间结构，而 Lotus 使用“预测接地”原则将时间序列数据与语言模型对齐。贝叶斯时间感知注意力 (BTA) 通过学习输入条件位置偏差来调整 Transformer 模型，而 VaryST 通过动态构建预测上下文将时空预测推广到闭世界设置之外。此外，CTIA-ODE 在 ODE 求解器中利用连续时间变量间注意力，DiffPTS 重新思考了概率预测的扩散 ELBO，TimeES 则采用演化谱进行概率性和确定性预测。 AI

影响 这些进展旨在提高 AI 模型在各个领域预测未来趋势的准确性和鲁棒性。

排序理由 arXiv 上发表的多篇研究论文介绍了时间序列预测的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 7 个来源。 我们如何撰写摘要 →

新研究应对长期时间序列预测挑战 · 跟踪 6 个来源

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arXiv 上发表的多篇研究论文介绍了时间序列预测的新方法。
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报道来源 [7]

  1. arXiv cs.AI TIER_1 English(EN) · Yangze Li, Zhengyang Zhou, Qihe Huang, Pengkun Wang, Binwu Wang, Yang Wang ·

    TStruct：为长期时间序列预测学习共享时序结构

    arXiv:2610.17864v1 Announce Type: cross Abstract: Long-term time series forecasting requires temporal dependency modeling that remains stable and generalizable under temporal distribution shifts. However, real-world time series are often noisy and non-stationary, making deep lear…

  2. arXiv cs.CL TIER_1 English(EN) · Van Dai Do, Huu Hiep Nguyen, Minh Hoang Nguyen, Manh Nguyen, Hung Le ·

    Lotus：通过学习预测来学习理解时间序列

    arXiv:2610.18877v1 Announce Type: cross Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervision encourages linguistic representations, it does not explicitly require them to …

  3. arXiv cs.LG TIER_1 English(EN) · Xinjie Lan, Yunbo Mei, Leheng Zhou, Hao Qi ·

    超越静态位置编码：用于非平稳长期时间序列预测的贝叶斯时间感知注意力机制

    arXiv:2610.19603v1 Announce Type: new Abstract: Transformers have demonstrated strong performance in long-term time series forecasting (LTSF), yet modeling non-stationary temporal dependencies remains challenging. In this paper, we interpret self-attention as a Bayesian posterior…

  4. arXiv cs.LG TIER_1 English(EN) · Shengnan Guo, Jinjun Cheng, Xiaowei Mao, Jianhao Zhang, Haochen Lv, Yang Jiang, YiHeng Huang, Yan Lin, Youfang Lin, Huaiyu Wan ·

    VaryST：超越闭世界设定的通用时空预测

    arXiv:2610.19813v1 Announce Type: new Abstract: Spatio-temporal forecasting is fundamental to many real-world systems, yet deployment environments are inherently varying: temporal distributions may evolve, spatial domains may change, and observation availability may shift from tr…

  5. arXiv cs.LG TIER_1 English(EN) · Chengyi Pan, Huangqimei Zheng, Hao Xue ·

    CTIA-ODE：用于长期时间序列预测的连续时间变量间注意力

    arXiv:2610.19831v1 Announce Type: new Abstract: Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging. Prevailing discrete-time architectures often impose rigid temporal alignment across he…

  6. arXiv cs.LG TIER_1 English(EN) · Weiwei Ye, Dongyuan Li, Hangchen Liu, Haotong Jiang, Yoshihide Sekimoto, Renhe Jiang ·

    DiffPTS：重新思考用于概率时间序列预测的扩散ELBO

    arXiv:2609.32363v2 Announce Type: replace Abstract: Probabilistic time series forecasting requires modeling and predicting complex and time-varying distributions. Recently, Denoising Diffusion Probabilistic Model (DDPM)-based approaches have shown promise by equipping the dif- fu…

  7. arXiv cs.LG TIER_1 English(EN) · Weiwei Ye, Renhe Jiang, Hangchen Liu, Dongyuan Li, Yoshihide Sekimoto ·

    TimeES：通过演化光谱进行概率和确定性时间序列预测

    arXiv:2609.32384v2 Announce Type: replace Abstract: Real-world time series are inherently non-stationary, with trends, periodic patterns, and uncertainty evolving over time. While the Fourier domain offers a natural lens to model time series, current deep learning approaches do n…

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