KAIROS无需自回归，实现快速精准的多峰时间序列预测。
KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting
- 直接建模分段多峰分布，避免误差累积
- 零样本泛化在6个基准上表现媲美顶尖模型
- 推理成本仅为同类模型的几分之一，适合实时系统
在万维网中，可靠的时间序列预测为资源规划、缓存放置和异常响应提供前瞻性信号，使平台能随用户行为和内容分布变化高效运行。与其它领域相比，网络应用的时间序列预测需更高响应速度以支持实时决策。本文提出KAIROS，一种非自回归时间序列预测框架，直接建模分段级多峰分布。不同于自回归方法，KAIROS避免误差累积，实现即时推理，同时优于现有非自回归模型的过平滑问题。在大规模语料上训练后，KAIROS在六个广泛使用的基准上展现强零样本泛化能力，性能接近同规模最先进基础模型，推理成本却仅为后者的几分之一。除了实证结果，KAIROS凸显了非自回归设计作为时间序列基础模型可扩展范式的重要性。
原文摘要 · Abstract (English)
In the World Wide Web, reliable time series forecasts provide the forward-looking signals that drive resource planning, cache placement, and anomaly response, enabling platforms to operate efficiently as user behavior and content distributions evolve. Compared with other domains, time series forecasting for Web applications requires much faster responsiveness to support real-time decision making. We present KAIROS, a non-autoregressive time series forecasting framework that directly models segment-level multi-peak distributions. Unlike autoregressive approaches, KAIROS avoids error accumulation and achieves just-in-time inference, while improving over existing non-autoregressive models that collapse to over-smoothed predictions. Trained on the large-scale corpus, KAIROS demonstrates strong zero-shot generalization on six widely used benchmarks, delivering forecasting performance comparable to state-of-the-art foundation models with similar scale, at a fraction of their inference cost. Beyond empirical results, KAIROS highlights the importance of non-autoregressive design as a scalable paradigm for foundation models in time series.
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