KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators

TL;DR

KODA is a novel data-driven recursive model that combines Koopman operators with Fourier filtering and data assimilation techniques to improve long-term forecasting and real-time state estimation of complex nonlinear dynamical systems.

Contribution

The paper introduces KODA, integrating Koopman operators with a recursive model and Fourier domain filtering for enhanced forecasting and data assimilation in nonstationary NLDS.

Findings

Outperforms state-of-the-art methods on multiple benchmarks.

Achieves stable and accurate long-term forecasts.

Effectively incorporates real-time measurements for data assimilation.

Abstract

Approaches based on Koopman operators have shown great promise in forecasting time series data generated by complex nonlinear dynamical systems (NLDS). Although such approaches are able to capture the latent state representation of a NLDS, they still face difficulty in long term forecasting when applied to real world data. Specifically many real-world NLDS exhibit time-varying behavior, leading to nonstationarity that is hard to capture with such models. Furthermore they lack a systematic data-driven approach to perform data assimilation, that is, exploiting noisy measurements on the fly in the forecasting task. To alleviate the above issues, we propose a Koopman operator-based approach (named KODA - Koopman Operator with Data Assimilation) that integrates forecasting and data assimilation in NLDS. In particular we use a Fourier domain filter to disentangle the data into a physical component whose dynamics can be accurately represented by a Koopman operator, and residual dynamics that represents the local or time varying behavior that are captured by a flexible and learnable recursive model. We carefully design an architecture and training criterion that ensures this decomposition lead to stable and long-term forecasts. Moreover, we introduce a course correction strategy to perform data assimilation with new measurements at inference time. The proposed approach is completely data-driven and can be learned end-to-end. Through extensive experimental comparisons we show that KODA outperforms existing state of the art methods on multiple time series benchmarks such as electricity, temperature, weather, lorenz 63 and duffing oscillator demonstrating its superior performance and efficacy along the three tasks a) forecasting, b) data assimilation and c) state prediction.