融合语言模型与知识图谱，提升时间序列预测与异常检测能力
A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph
- 用知识图谱增强提示词，结合时序补丁与文本描述进行多任务学习
- 在多个基准数据集上显著优于现有方法，仅需少量可训练参数
- 适合需要融合领域知识的时间序列分析场景，如金融、工业监控
时间序列分析在金融、交通、工业等领域至关重要。传统模型仅依赖时序特征，难以捕捉深层信息。本文提出一种新型多任务框架LTM，融合预训练时间序列模型、大语言模型（LLM）和知识图谱，实现预测、填补与异常检测。LTM将时间序列编码为补丁，利用知识图谱增强用户提示词，通过创新的特征融合机制将提示嵌入各补丁编码中，经冻结的LLM处理后，由特征增强模块和时间解码器完成任务。微调阶段引入提示与时间补丁间的余弦相似度作为损失项，提升性能。实验表明，LTM在多个基准数据集上显著优于现有方法，具备高效性与实用性。
原文摘要 · Abstract (English)
Time series analysis is crucial in fields like finance, transportation, and industry. However, traditional models often focus solely on temporal features, limiting their ability to capture underlying information. This paper proposes a novel time series multitask framework, called LTM, which integrates temporal features with textual descriptions to enhance analytical and predictive capabilities. LTM combines pre-trained time series model, large language model (LLM), and knowledge graph to tackle time series tasks, including forecasting, imputation, and anomaly detection. LTM achieves improved performance with a few trainable parameters. It is very efficient and practical. LTM encodes time series data into patches and enriches user-provided prompts using knowledge graphs to generate enhanced prompts. A novel feature fusion method embeds prompts into each patch encoding, which is processed by a frozen LLM, followed by a feature enhancement module and a time decoder module. During fine-tuning stage, cosine similarity between prompts and temporal patches is integrated into the loss function to boost performance. Experiments on benchmark datasets show that LTM significantly outperforms existing methods. It provides a robust and versatile solution for time series tasks.
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