Topological Kalman Filtering on Cell Complexes

TL;DR

This paper introduces a topology-aware Kalman filtering framework on cell complexes for inferring latent dynamics from complex, high-dimensional, and partially observable multivariate time-series data.

Contribution

It develops a novel state space model based on stochastic PDEs on cell complexes, incorporating topological diffusion and a recursive estimation method with structure inference.

Findings

Reliable latent state estimation under partial observability.

Successful recovery of underlying topological structures.

Effective validation on synthetic and real-world datasets.

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 transportation networks. However, reconstructing these latent states is challenging because the signals are coupled across higher-order topologies, while high dimensionality, nonlinear observations, and unknown structures increase the difficulty. To address this, we propose a topology-aware state space framework derived from stochastic partial differential equations on cell complexes. State evolution follows heat-like topological diffusion, with perturbations propagating along boundary operators. Under partial observability, we model observations using a cell complex convolution of latent states coupled with a nonlinear mapping. We perform recursive state estimation via an Extended Kalman Filter, simultaneously learning model parameters and uncertainties through an online Expectation-Maximization algorithm. Finally, for scenarios where only lower-order topological structure is known, e.g., nodes and edges, as in critical infrastructure networks, we introduce a heuristic cell identification algorithm to explicitly infer the second-order cell structures. Validations on synthetic and real datasets from water, sensor and transportation networks demonstrate that our approach yields reliable estimates under partial observability and successfully recovers the underlying topological structures.