PCD: Predictive Coding Dynamics for Irregularly Sampled Time Series
Tianrui Gou, Jiayangmei Xu, Xingzhou Zhang, Lei Sha
Abstract
Dynamic systems evolve continuously over time, whereas observations in real scenarios are often sparse, irregular, and asynchronous. Accurate prediction therefore requires inferring latent state evolution between observations, as future values depend on partially observed trajectories. We propose Predictive Coding Dynamics (PCD), a residual feedback mechanism for irregular continuous dynamic models. PCD maintains previous reconstructions, predicts observed entries from them, and incorporates masked signed residuals into state recognition through an energy penalty. The mechanism is implemented with ODE-RNN and Latent ODE, supporting event-based hidden updates and latent state recognition. Experiments on forecasting and trajectory recovery tasks show that PCD improves continuous dynamic backbones under sparse and irregular sampling. The results further indicate that predictive error feedback improves latent state estimation when observations constrain the current trajectory. These results suggest that PCD provides a residual feedback framework that jointly supports trajectory generation, observation consistency checking, and state refinement in continuous-time series modeling. The source code is available at https://anonymous.4open.science/r/PCD-for-paper-92F1/.