用可解释的突变感知方法，让时间序列中的异常更易发现。
Interpretable Transformation and Analysis of Timelines through Learning via Surprisability
- 基于人类注意力认知的突变性理论，量化时间序列中的意外偏离。
- 在传感器、死亡原因和总统演讲三类数据中均有效识别出关键异常点。
- 适合需要理解异常背后原因的研究者，尤其适用于高维时间数据场景。
高维时间序列数据分析及异常检测在传感器读数、生物医学数据、历史记录和全球统计等领域至关重要。然而，传统方法常受限于高维度、复杂分布与稀疏性，难以有效提取有意义的洞察。受认知科学中‘突变性’（surprisability）启发——即人类本能关注意外偏差——我们提出学习突变性（Learning via Surprisability, LvS）方法，通过形式化预期行为的偏离来量化并优先排序异常。LvS将认知注意力理论与计算方法结合，实现对异常和趋势变化的可解释检测，同时保留关键上下文信息。我们在三个高维时间序列场景中验证：传感器数据、多年全球死亡原因数据集，以及包含两个世纪美国国情咨文的文本语料库。结果表明，LvS转换能高效且可解释地识别出异常、异常点及随时间变化最显著的特征。
原文摘要 · Abstract (English)
The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medical data, historical records, and global statistics. However, conventional analysis techniques often struggle with challenges such as high dimensionality, complex distributions, and sparsity. These limitations hinder the ability to extract meaningful insights from complex temporal datasets, making it difficult to identify trending features, outliers, and anomalies effectively. Inspired by surprisability -- a cognitive science concept describing how humans instinctively focus on unexpected deviations - we propose Learning via Surprisability (LvS), a novel approach for transforming high-dimensional timeline data. LvS quantifies and prioritizes anomalies in time-series data by formalizing deviations from expected behavior. LvS bridges cognitive theories of attention with computational methods, enabling the detection of anomalies and shifts in a way that preserves critical context, offering a new lens for interpreting complex datasets. We demonstrate the usefulness of LvS on three high-dimensional timeline use cases: a time series of sensor data, a global dataset of mortality causes over multiple years, and a textual corpus containing over two centuries of State of the Union Addresses by U.S. presidents. Our results show that the LvS transformation enables efficient and interpretable identification of outliers, anomalies, and the most variable features along the timeline.
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