面向长上下文时间序列基础模型
Towards Long-Context Time Series Foundation Models
AI总结：
本研究通过引入压缩记忆机制，使仅编码器时间序列基础模型能处理长多变量序列，并在MOMENT模型上验证了其有效性。
摘要：
时间序列基础模型在各种任务和广泛领域中都展现出了令人印象深刻的表现，即使在零样本设置下也是如此。然而，这些模型中的大多数被设计为处理短的单变量时间序列作为输入。这限制了它们的实际应用，尤其是在医疗保健等领域，这些领域拥有大量长且多变量的数据，且存在强时间依赖和变量内依赖。我们的研究通过编目并系统比较来自语言和时间序列领域的各种上下文扩展技术，并引入一种新颖的压缩记忆机制，使仅编码器的时间序列基础模型能够有效建模变量内依赖，从而弥合了这一差距。我们通过将多任务时间序列基础模型家族MOMENT赋予多变量上下文，展示了我们方法的优势。
英文摘要：
Time series foundation models have shown impressive performance on a variety of tasks, across a wide range of domains, even in zero-shot settings. However, most of these models are designed to handle short univariate time series as an input. This limits their practical use, especially in domains such as healthcare with copious amounts of long and multivariate data with strong temporal and intra-variate dependencies. Our study bridges this gap by cataloging and systematically comparing various context expansion techniques from both language and time series domains, and introducing a novel compressive memory mechanism to allow encoder-only TSFMs to effectively model intra-variate dependencies. We demonstrate the benefits of our approach by imbuing MOMENT, a recent family of multi-task time series foundation models, with the multivariate context.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
