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[2602.16864] Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Summary
This paper argues for the integration of dynamical systems theory into time series modeling to enhance forecasting accuracy and efficiency, highlighting its potential benefits and applications.
Why It Matters
As time series modeling evolves, understanding the underlying dynamical systems can significantly improve forecasting capabilities. This perspective not only promises better short-term predictions but also aids in understanding long-term system behaviors, which is crucial for various industries relying on accurate data analysis.
Key Takeaways
- Dynamical systems theory can enhance time series modeling by providing deeper insights into underlying mechanisms.
- Incorporating dynamical systems can lead to more accurate long-term forecasts and reduced computational costs.
- The paper suggests practical methods for integrating dynamical systems insights into existing time series models.
Computer Science > Machine Learning arXiv:2602.16864 (cs) [Submitted on 18 Feb 2026] Title:Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling Authors:Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess, Charlotte Ricarda Doll, Lukas Eisenmann View a PDF of the paper titled Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling, by Daniel Durstewitz and 4 other authors View PDF HTML (experimental) Abstract:Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial demand in this field, it is not always clear how much progress there really is. To advance TS forecasting and analysis to the next level, here we argue that the field needs a dynamical systems (DS) perspective. TS of observations from natural or engineered systems almost always originate from some underlying DS, and arguably access to its governing equations would yield theoretically optimal forecasts. This is the promise of DS reconstruction (DSR), a class of ML/AI approaches that aim to infer surrogate models of the underlying DS from data. But models based on DS principles offer other profound advantages: Beyond short-term forecasts, they enable to predict the long-term statistics of an observed system, which in many practical scenarios may be the more relevant quantities. DS theory furthermore provides domain-independent ...