François-Xavier Aubet, Daniel Zügner, Jan Gasthaus
TL;DR本研究提出了一种结合 Monte Carlo EM 技术的神经网络模型,能够显式地识别和处理时间序列数据中的异常点,通过将潜在的异常点指示变量引入到模型中,提高了模型在标准数据集上的表现。
Abstract
time series data are often corrupted by outliers or other kinds of anomalies.
Identifying the anomalous points can be a goal on its own (anomaly detection),
or a means to improving performance of other time serie