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新的卡尔曼滤波框架在细胞复形上对复杂时间序列数据进行建模

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的拓扑感知状态空间框架，用于从复杂的时间序列数据中推断潜在动力学。该方法利用细胞复形上的随机偏微分方程来模拟状态演化和观测，即使在部分可观测和结构未知的情况下也是如此。该方法采用扩展卡尔曼滤波器进行递归状态估计，并采用期望最大化算法进行参数学习，同时使用启发式算法来推断缺失的拓扑结构。 AI

影响 引入了一个分析复杂、互联数据的新颖框架，有可能改进智能电网和交通管理等系统的推理。

排序理由 该集群包含一篇详细介绍时间序列分析新颖方法的学术论文。

在 arXiv stat.ML 阅读 →

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新的卡尔曼滤波框架在细胞复形上对复杂时间序列数据进行建模

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Chengen Liu, Rohan Money, Ting Gao, Mohammad Sabbaqi, Baltasar Beferull-Lozano, Elvin Isufi ·

    Kalman Filtering on Cell Complexes

    arXiv:2605.15955v1 Announce Type: cross Abstract: Inferring latent dynamics from multivariate time-series defined over topological cell complexes is crucial for capturing the complex, higher-order interactions inherent in real-world systems such as in water, sensor, and transport…

  2. arXiv stat.ML TIER_1 English(EN) · Elvin Isufi ·

    Kalman Filtering on Cell Complexes

    Inferring latent dynamics from multivariate time-series defined over topological cell complexes is crucial for capturing the complex, higher-order interactions inherent in real-world systems such as in water, sensor, and transportation networks. However, reconstructing these late…

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