Interpretable Transformation and Analysis of Timelines through Learning via Surprisability
通过惊异度学习实现时间线数据的可解释变换与分析
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…