将多维时间序列转为可解释事件序列，提升分析精度与可读性。
Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining
- 通过可变长度元组挖掘，将多维时间序列转为一维事件序列。
- 无需大量训练数据，在动作分类任务中表现优异。
- 适合医疗监测、用户行为分析等需可解释性的场景。
本文提出一种新型时空特征表示模型，解决传统多维时间序列（MTS）分析方法的局限性。该方法将MTS转换为一维的空间演化事件序列，保留各维度间的复杂耦合关系。通过可变长度元组挖掘技术提取关键时空特征，提升了时间序列分析的可解释性与准确性。不同于传统模型，该无监督方法不依赖大规模训练数据，具备跨领域适应能力。运动序列分类实验验证了其在捕捉复杂数据模式上的优越性能。该框架在IT基础设施监控与优化、连续患者健康监测与趋势分析、用户行为追踪与销售预测等场景具有广泛应用潜力。本研究为时间序列数据挖掘及其在人类行为识别等领域的应用提供了新的理论基础与技术支持。
原文摘要 · Abstract (English)
This paper introduces a novel spatiotemporal feature representation model designed to address the limitations of traditional methods in multidimensional time series (MTS) analysis. The proposed approach converts MTS into one-dimensional sequences of spatially evolving events, preserving the complex coupling relationships between dimensions. By employing a variable-length tuple mining method, key spatiotemporal features are extracted, enhancing the interpretability and accuracy of time series analysis. Unlike conventional models, this unsupervised method does not rely on large training datasets, making it adaptable across different domains. Experimental results from motion sequence classification validate the model's superior performance in capturing intricate patterns within the data. The proposed framework has significant potential for applications across various fields, including backend services for monitoring and optimizing IT infrastructure, medical diagnosis through continuous patient monitoring and health trend analysis, and internet businesses for tracking user behavior and forecasting sales. This work offers a new theoretical foundation and technical support for advancing time series data mining and its practical applications in human behavior recognition and other domains.
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