提出可解释时序数据处理框架，融合数值与事件数据，提升模型可理解性。
Towards Explainable Sequential Learning
- 通过后验分析将数值输入分解为并发成分，实现可解释性
- 在多变量时间序列分类任务中超越现有最优方法
- 适合需要透明决策过程的医疗、金融等场景
本文提出一种混合可解释时序数据处理流程——数据驱动可解释多变量相关时序人工智能（EMeriTAte+DF），通过验证的人工智能原理，连接数值驱动与事件驱动的时序分类任务，实现人类可理解的结果。该方法首先通过后验可解释阶段，将数值输入数据以具有数值载荷的并发成分进行描述；进而扩展事件驱动文献，设计支持并发成分的规范挖掘算法。此前及当前方案在多变量时间序列分类任务中均优于现有最先进方法，验证了所提方法的有效性。
原文摘要 · Abstract (English)
This paper offers a hybrid explainable temporal data processing pipeline, DataFul Explainable MultivariatE coRrelatIonal Temporal Artificial inTElligence (EMeriTAte+DF), bridging numerical-driven temporal data classification with an event-based one through verified artificial intelligence principles, enabling human-explainable results. This was possible through a preliminary a posteriori explainable phase describing the numerical input data in terms of concurrent constituents with numerical payloads. This further required extending the event-based literature to design specification mining algorithms supporting concurrent constituents. Our previous and current solutions outperform state-of-the-art solutions for multivariate time series classifications, thus showcasing the effectiveness of the proposed methodology.
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