一种时间感知的感受野袋方法用于可解释的不规则时间序列分类
A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification
- University of Pisa（比萨大学）
- ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究扩展BORF至不规则时间序列，提出时间加权归一化方案，保持线性复杂度，在PYRREGULAR数据集上达到竞争性能并提供可解释性。
AI中文摘要：
不规则时间序列以非均匀采样间隔、缺失观测和可变长度为特征，在医疗保健、移动性和环境监测中普遍存在，然而针对该设置的有效且可解释的分类器有限。现有方法通常依赖插补，这可能掩盖数据的时间结构，或需要复杂且不透明、难以解释的神经架构。在本工作中，我们将感受野袋（BORF）——一种快速、确定且可解释的时间序列变换——扩展到不规则设置。我们的关键贡献是一种时间加权归一化方案，其中每个观测按其关联的时间增量成比例加权，使得模式提取对样本的实际时间分布敏感，而不仅仅是其索引位置。这需要推导时间加权标准差的高效滑动窗口递推，以保持BORF的线性时间复杂度。我们在PYRREGULAR仓库的数据集上将所得方法与最先进的不规则时间序列分类器进行基准测试，展示了具有竞争力的分类性能，并额外提供了人类可解释的解释。
英文摘要：
Irregular time series, characterized by non-uniform sampling intervals, missing observations, and variable lengths, are ubiquitous in healthcare, mobility, and environmental monitoring, yet effective and interpretable classifiers for this setting are limited. Existing approaches often rely on imputation, which can obscure the temporal structure of the data, or require complex neural architectures that are opaque and difficult to explain. In this work, we extend the Bag-Of-Receptive-Fields (BORF), a fast, deterministic, and interpretable transform for time series, to the irregular setting. Our key contribution is a time-weighted normalization scheme in which each observation is weighted proportionally to its associated time delta, making pattern extraction sensitive to the actual temporal distribution of samples rather than only their index position. This requires deriving an efficient sliding-window recurrence for the time-weighted standard deviation, preserving the linear time complexity of BORF. We benchmark the resulting method against state-of-the-art irregular time series classifiers on datasets from the PYRREGULAR repository, demonstrating competitive classification performance with the added benefit of human-interpretable explanations.
