Summary of Koda: a Data-driven Recursive Model For Time Series Forecasting and Data Assimilation Using Koopman Operators, by Ashutosh Singh et al.
KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators
by Ashutosh Singh, Ashish Singh, Tales Imbiriba, Deniz Erdogmus, Ricardo Borsoi
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Koopman operator-based approach, named KODA, is proposed to address long-term forecasting challenges in complex nonlinear dynamical systems (NLDS). Existing methods can capture latent state representations but struggle with nonstationarity and lack data-driven approaches for assimilating noisy measurements. KODA integrates forecasting and data assimilation by decomposing time series into physical components and residual dynamics using Fourier domain filters and recursive models. The approach is trained end-to-end and demonstrates superior performance on multiple benchmarks, including electricity, temperature, weather, Lorenz 63, and Duffing oscillator datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to predict the future behavior of a complex system, like the weather or stock market. Right now, we can’t do that very well because our methods get stuck in the past and don’t account for new information coming in. This paper proposes a new way to forecast the future by combining two ideas: understanding how systems work and incorporating new data as it becomes available. The approach is called KODA and uses special filters to separate the system’s physical behavior from random fluctuations. By doing this, we can make more accurate predictions over longer periods of time. In tests, KODA outperformed existing methods on various datasets. |
Keywords
* Artificial intelligence * Temperature * Time series
