Kairos: Towards Adaptive and Generalizable Time Series Foundation Models

• URL: http://arxiv.org/abs/2509.25826v1
• Date: Tue, 30 Sep 2025 06:02:26 GMT
• Title: Kairos: Towards Adaptive and Generalizable Time Series Foundation Models
• Authors: Kun Feng, Shaocheng Lan, Yuchen Fang, Wenchao He, Lintao Ma, Xingyu Lu, Kan Ren,
• Abstract summary: Time series foundation models (TSFMs) have emerged as a powerful paradigm for time series analysis.<n>We propose Kairos, a flexible TSFM framework that integrates a dynamic patching tokenizer and an instance-adaptive positional embedding.<n>Kairos achieves superior performance with much fewer parameters on two common zero-shot benchmarks.
• Score: 27.076542021368056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series foundation models (TSFMs) have emerged as a powerful paradigm for time series analysis, driven by large-scale pretraining on diverse data corpora. However, time series inherently exhibit heterogeneous information density over time, influenced by system states and signal complexity, presenting significant modeling challenges especially in a zero-shot scenario. Current TSFMs rely on non-adaptive processing pipelines that fail to capture this dynamic nature. For example, common tokenization strategies such as fixed-size patching enforce rigid observational granularity, limiting their ability to adapt to varying information densities. Similarly, conventional positional encodings impose a uniform temporal scale, making it difficult to model diverse periodicities and trends across series. To overcome these limitations, we propose Kairos, a flexible TSFM framework that integrates a dynamic patching tokenizer and an instance-adaptive positional embedding. Kairos adaptively selects tokenization granularity and tailors positional encodings to the unique characteristics of each time series instance. Trained on a large-scale Predictability-Stratified Time Series (PreSTS) corpus comprising over 300 billion time points and adopting a multi-patch prediction strategy in the inference stage, Kairos achieves superior performance with much fewer parameters on two common zero-shot benchmarks, GIFT-Eval and the Time-Series-Library benchmark, consistently outperforming established methods across diverse tasks. The project page is at https://foundation-model-research.github.io/Kairos .

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