Combining Dynamic Mode Decomposition with Ensemble Kalman Filtering for Tracking and Forecasting

TL;DR

This paper introduces a novel method combining dynamic mode decomposition with ensemble Kalman filtering to effectively track and forecast high-dimensional dynamical systems using data-driven models, demonstrated on synthetic and real influenza data.

Contribution

The paper presents the DMDEnKF, a new approach that integrates DMD with ensemble Kalman filtering for data-driven modeling and real-time updating of high-dimensional systems.

Findings

DMDEnKF accurately tracks synthetic dynamical systems.

The method performs comparably to mechanistic models in influenza forecasting.

Time-delay embeddings enhance the model's capabilities.

Abstract

Data assimilation techniques, such as ensemble Kalman filtering, have been shown to be a highly effective and efficient way to combine noisy data with a mathematical model to track and forecast dynamical systems. However, when dealing with high-dimensional data, in many situations one does not have a model, so data assimilation techniques cannot be applied. In this paper, we use dynamic mode decomposition to generate a low-dimensional, linear model of a dynamical system directly from high-dimensional data, which is defined by temporal and spatial modes, that we can then use with data assimilation techniques such as the ensemble Kalman filter. We show how the dynamic mode decomposition can be combined with the ensemble Kalman filter (which we call the DMDEnKF) to iteratively update the current state and temporal modes as new data becomes available. We demonstrate that this approach is able to track time varying dynamical systems in synthetic examples, and experiment with the use of time-delay embeddings. We then apply the DMDEnKF to real world seasonal influenza-like illness data from the USA Centers for Disease Control and Prevention, and find that for short term forecasting, the DMDEnKF is comparable to the best mechanistic models in the ILINet competition.