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English(EN) Multivariate Time Series Forecasting with Adaptive Non-Local Observables

新框架和量子模型推动时间序列预测发展 · 跟踪 5 个来源

作者 PulseAugur 编辑部 · [5 个来源] ·

研究人员正通过新框架和模型推动时间序列预测的发展。其中一种方法 WrapFlow 使用连续时间建模和标记化来处理不规则数据，取得了最先进的结果。另一项进展涉及量子神经网络，MTSF-ANO 集成了变分量子电路和自适应非局部可观测量，以提高预测准确性。此外，受大型语言模型启发的基金模型概念正在被探索用于时间序列预测，提供了一种可以通过微调进一步增强的统一方法。 AI

影响 时间序列预测的这些进展可能导致金融、医疗保健和环境监测等领域的预测更加准确，从而改善决策和资源分配。

排序理由 该集群包含多篇详细介绍时间序列预测新模型和框架的学术论文。

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新框架和量子模型推动时间序列预测发展 · 跟踪 5 个来源

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该集群包含多篇详细介绍时间序列预测新模型和框架的学术论文。
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报道来源 [5]

  1. arXiv cs.LG TIER_1 English(EN) · Tianen Shen, Zhengyu Li, Yutong Li, Xiangfei Qiu, Xingjian Wu, Bin Yang, Jilin Hu ·

    利用连续时间建模框架增强不规则时间序列预测

    arXiv:2607.28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing. Their core challenges stem from asynchronous observations, non-uniform s…

  2. arXiv cs.AI TIER_1 English(EN) · Yu-Ting Lee, Huan-Hsin Tseng, Samuel Yen-Chi Chen ·

    自适应非局部可观测量的多变量时间序列预测

    arXiv:2607.24399v1 Announce Type: cross Abstract: Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measureme…

  3. arXiv cs.LG TIER_1 English(EN) · Morad Laglil, Bertrand Pracca, Emilie Devijver, Eric Gaussier ·

    基础模型与微调：迈向新一代时间序列预测模型

    arXiv:2607.23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    自适应非局部可观测量的多变量时间序列预测

    Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measurements, which restrict their expressivity. We propose…

  5. arXiv stat.ML TIER_1 English(EN) · Ziling Ma, Junshu Jiang, \'Angel L\'opez-Oriona, Ying Sun, Hernando Ombao ·

    面向多变量非平稳时间序列的自适应多尺度预测与门控局部一致性预测

    arXiv:2607.23165v1 Announce Type: new Abstract: We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The central idea is to learn an adaptive predictive state representation for point forecasti…

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