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New Kalman Filter framework models complex time-series data on cell complexes

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new topology-aware state space framework for inferring latent dynamics from complex time-series data. This approach utilizes stochastic partial differential equations on cell complexes to model state evolution and observations, even with partial observability and unknown structures. The method employs an Extended Kalman Filter for recursive state estimation and an Expectation-Maximization algorithm for parameter learning, with a heuristic algorithm to infer missing topological structures. AI

IMPACT Introduces a novel framework for analyzing complex, interconnected data, potentially improving inference in systems like smart grids and traffic management.

RANK_REASON The cluster contains an academic paper detailing a novel methodology for time-series analysis.

Read on arXiv stat.ML →

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

New Kalman Filter framework models complex time-series data on cell complexes

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The cluster contains an academic paper detailing a novel methodology for time-series analysis.
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COVERAGE [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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