Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems
This addresses a challenge in data-driven prediction for systems with incomplete observations, such as in traffic monitoring, but appears incremental as it adapts existing methods to a specific bottleneck.
The paper tackles the problem of predicting partially observed systems using a recurrent neural network, developing a predictor that combines an echo-state network with time delay embedding, and demonstrates its efficacy on synthetic chaotic datasets and real-time traffic data.
This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial observation during training phase poses a significant challenge. Here a predictor for partial observations is developed using an echo-state network (ESN) and time delay embedding of the partially observed state. The proposed method is theoretically justified with Taken's embedding theorem and strong observability of a nonlinear system. The efficacy of the proposed method is demonstrated on three systems: two synthetic datasets from chaotic dynamical systems and a set of real-time traffic data.