# Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems

@article{Dylewsky2022StochasticallyFE, title={Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems}, author={Daniel Dylewsky and David A. Barajas-Solano and Tong Ma and Alexandre M. Tartakovsky and J. Nathan Kutz}, journal={IEEE Access}, year={2022}, volume={10}, pages={33440-33448} }

Time series forecasting remains a central challenge problem in almost all scientific disciplines. We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system using Dynamic Mode Decomposition (DMD) in time delay coordinates. Central to this approach is the insight that grid load, like many observables on complex real-world systems, has an “almost-periodic” character, i.e., a continuous Fourier spectrum punctuated by dominant peaks, which capture…

## 2 Citations

O N THE USE OF DYNAMIC MODE DECOMPOSITION FOR TIME - SERIES FORECASTING OF SHIPS MANEUVERING IN WAVES

- Engineering
- 2022

A statistical analysis on the use of dynamic mode decomposition (DMD) and its augmented variant, via state augmentation, as data-driven and equation-free modeling approach for the prediction of…

On the use of dynamic mode decomposition for time-series forecasting of ships operating in waves

- Engineering
- 2022

A data-driven and equation-free modeling approach for forecasting of trajectories, motions, and forces of ships in waves is presented, based on dynamic mode decomposition (DMD). A statistical…

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