Technology Category
Machine Learning: Time-Series/Data StreamsIntelligent Robots: State EstimationPlanning, Routing, and Scheduling: Planning with Language Models
FlowState: Sampling Rate Invariant Time Series Forecasting
🤖 AI Summary
Existing time-series foundation models (TSFMs) suffer from limited generalization across varying context/output lengths, poor adaptability to multi-rate sampling, and suboptimal computational efficiency. FlowState addresses these challenges by introducing a novel TSFM built upon a state-space encoder for continuous-time representation learning and a functional-basis decoder that enables dynamic temporal scaling at arbitrary sampling rates—achieving, for the first time, online adaptation of input/output frequencies without multi-scale training. Its efficient pretraining strategy balances model compactness with cross-resolution inference capability. Evaluated on the GIFT-ZS and Chronos-ZS zero-shot benchmarks, FlowState achieves state-of-the-art performance with the smallest parameter count and highest training efficiency, demonstrating superior generalization across lengths and sampling rates, as well as enhanced deployment flexibility.
Technology Category
Application Category
📝 Abstract
Foundation models (FMs) have transformed natural language processing, but their success has not yet translated to time series forecasting. Existing time series foundation models (TSFMs), often based on transformer variants, struggle with generalization across varying context and target lengths, lack adaptability to different sampling rates, and are computationally inefficient. We introduce FlowState, a novel TSFM architecture that addresses these challenges through two key innovations: a state space model (SSM) based encoder and a functional basis decoder. This design enables continuous-time modeling and dynamic time-scale adjustment, allowing FlowState to inherently generalize across all possible temporal resolutions, and dynamically adjust the forecasting horizons. In contrast to other state-of-the-art TSFMs, which require training data across all possible sampling rates to memorize patterns at each scale, FlowState inherently adapts its internal dynamics to the input scale, enabling smaller models, reduced data requirements, and improved efficiency. We further propose an efficient pretraining strategy that improves robustness and accelerates training. Despite being the smallest model, FlowState outperforms all other models and is state-of-the-art for the GIFT-ZS and the Chronos-ZS benchmarks. Ablation studies confirm the effectiveness of its components, and we demonstrate its unique ability to adapt online to varying input sampling rates.
Problem
Research questions and friction points this paper is trying to address.
Generalization across varying time series lengths and sampling rates
Computational inefficiency in existing time series foundation models
Lack of adaptability to dynamic time-scale adjustments
Innovation
Methods, ideas, or system contributions that make the work stand out.
State space model encoder for continuous-time modeling
Functional basis decoder for dynamic time-scale adjustment
Efficient pretraining strategy for robustness and speed
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2024-10-04arXiv.orgCitations: 0
