arXiv:2606.10085v1 Announce Type: new Abstract: Matrix-valued time series arise in a wide range of applications, such as spatio-temporal data from medical imaging and geophysics. Existing methods are mainly designed for static settings and lack adaptability to streaming and time-varying environments. Adaptive filtering techniques have also been largely limited to data with scalar or vector values, leaving adaptive forecasting for matrix-valued time series inadequately understood. To bridge these gaps, we develop an adaptive tensor regression framework that includes Matrix-on-Matrix (MoM) and T
The increasing prevalence of high-dimensional, time-varying data across scientific and industrial applications is driving the need for more sophisticated analytical tools.
This development allows for more accurate and adaptive forecasting of complex data streams, leading to better decision-making in critical fields like medical imaging and geophysics.
The ability to perform robust adaptive forecasting on matrix-valued time series will significantly enhance predictive modeling capabilities beyond current scalar and vector approaches.
- · AI/ML researchers
- · Medical imaging sector
- · Geophysics industry
- · Data science platforms
- · Legacy statistical methods
- · Companies reliant on static data models
Improved accuracy in spatio-temporal predictions for healthcare diagnostics and resource exploration.
Accelerated development of AI systems that can learn and adapt in real-time from complex streaming data.
Potential for new autonomous systems to operate more effectively in dynamic, high-dimensional environments.
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Read at arXiv cs.LG