arXiv:2602.16864v2 Announce Type: replace 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
The proliferation of complex time series data across numerous fields and the limitations of current 'foundation model' approaches are driving a search for more robust analytical frameworks.
A shift towards dynamical systems in time series offers a more rigorous and explainable approach to modeling phenomena, crucial for critical applications ranging from finance to climate and fundamental AI research.
The emphasis in time series modeling may shift from purely data-driven, black-box approaches to methods that explicitly incorporate underlying physical or systemic dynamics, leading to more transparent and reliable predictions.
- · Expertise in dynamical systems theory
- · AI researchers in academia
- · Applications requiring high-stakes forecasting
- · Physics-informed AI models
- · Purely statistical time series methods
- · AI models lacking interpretability
- · Organizations over-reliant on 'hype-driven' AI
Increased focus on causality and underlying mechanisms in time series analysis leads to more stable and explainable AI models.
New interdisciplinary research bridging applied mathematics, physics, and machine learning accelerates discoveries in complex systems modeling.
Industries reliant on forecasting, such as finance, logistics, and resource management, adopt more robust and less error-prone predictive systems, potentially altering market stability and efficiency.
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