SPEAR：面向时序数据的软提示增强异常识别
SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Data
AI总结：
针对传统时序异常检测方法难处理变长序列与上下文异常的问题，提出结合软提示与量化的SPEAR方法，将量化时序嵌入与可学习软提示结合输入冻结LLM，经交叉熵损失迭代更新软提示，有效提升了LLM时序异常检测性能。
摘要：
时序异常检测在医疗健康、互联网流量监控等诸多领域发挥着关键作用。大语言模型（LLM）的出现为海量无处不在的时序数据中的异常检测提供了新的机遇。传统方法难以应对变长时序序列以及基于上下文的异常问题。我们提出了软提示增强异常识别（Soft Prompt Enhanced Anomaly Recognition，SPEAR），这是一种借助软提示与量化技术利用LLM开展异常检测的新方法。我们的方法流程包括：将时序数据量化并转换为输入嵌入，再将其与可学习的软提示嵌入相结合，随后将这些组合嵌入输入到冻结的LLM中。软提示会基于交叉熵损失进行迭代更新，使模型能够适配时序异常检测任务。软提示的使用有助于LLM有效适配时序任务，而量化技术则确保了序列处理的最优性——因为LLM本身是为处理离散序列设计的。我们的实验结果表明，软提示有效提升了LLM在时序异常检测下游任务中的性能。
英文摘要：
Time series anomaly detection plays a crucial role in a wide range of fields, such as healthcare and internet traffic monitoring. The emergence of large language models (LLMs) offers new opportunities for detecting anomalies in the ubiquitous time series data. Traditional approaches struggle with variable-length time series sequences and context-based anomalies. We propose Soft Prompt Enhanced Anomaly Recognition (SPEAR), a novel approach to leverage LLMs for anomaly detection with soft prompts and quantization. Our methodology involves quantizing and transforming the time series data into input embeddings and combining them with learnable soft prompt embeddings. These combined embeddings are then fed into a frozen LLM. The soft prompts are updated iteratively based on a cross-entropy loss, allowing the model to adapt to time series anomaly detection. The use of soft prompts helps adapt LLMs effectively to time series tasks, while quantization ensures optimal handling of sequences, as LLMs are designed to handle discrete sequences. Our experimental results demonstrate that soft prompts effectively increase LLMs' performance in downstream tasks regarding time series anomaly detection.
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。
