arXiv:2508.05287v3 Announce Type: replace-cross Abstract: Existing time series foundation models (TSFMs), often based on transformer variants, lack adaptability to different sampling rates, struggle with generalization across varying context and target lengths, and are computationally inefficient. We introduce FlowState, a novel TSFM architecture that achieves sampling-rate-equivariant forecasting through a unified design that pairs a state space model (SSM) encoder with a functional basis decoder (FBD). This design enables continuous-time modeling and dynamic time-scale adjustment, allowing F
The proliferation of various time series data, coupled with the computational demands of existing models, creates a strong incentive for more efficient and adaptable AI architectures.
This development addresses key shortcomings in current time series forecasting, offering a potential leap in AI model efficiency and adaptability for critical applications.
Time series forecasting models may become significantly more robust to varying data granularities and computational constraints, enabling wider deployment and more reliable predictions in complex systems.
- · AI model developers
- · Industries relying on time series analysis (e.g., finance, logistics, healthcare
- · Cloud computing providers (due to potential for more widespread adoption of AI w
- · Developers of less adaptable or computationally intensive TSFMs
- · Early investors in legacy time series forecasting solutions
Improved accuracy and efficiency in predictions across various domains from autonomous systems to financial markets.
Reduced operational costs and increased automation in sectors heavily reliant on time-series data analysis.
Accelerated development of real-time intelligent systems that can dynamically adapt to environmental changes with high precision.
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Read at arXiv cs.AI