Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

Deep neural networks, especially the Convolutional Neural Network (CNN) models, have
shown promising results in multivariate time series data analysis. However, the predictions …

Conventional time series classification approaches based on bags of patterns or shapelets
face significant challenges in dealing with a vast amount of feature candidates from high …

When we recognize images with the help of Artificial Neural Networks (ANNs), we often
wonder how they make decisions. A widely accepted solution is to point out local features as …

Epilepsy is an intractable chronic neurological disease attached to extensive attention. Due
to the fact that unpredictable seizure attacks result in serious physical injuries, early warning …

Mobile applications have increasingly entered the healthcare sector. Besides being daily
companions, so-called mHealth applications have the potential to enable individuals to …

Due to the fact of uncertainty contained in observed real-valued time series, the aim of this
paper is to explore interval implicitization in real-valued time series classification problems …

针对RoQ 攻击隐藏在海量背景流量中难以识别, 且现有样本稀少无法提供大规模学习数据的
问题, 提出了在极少先验知识条件下基于多层次特征的RoQ 隐蔽攻击无监督检测方法. 首先 …

针对现有Shapelet 提取方法无法反映趋势特点, 提取结果与原始数据偏离程度略大的问题,
提出了一种改进的快速Shapelet 选择算法. 本文首先提出了一种考虑时间序列相对趋势的距离 …

A promising way for the waterborne industry towards decarbonization and emissions
reduction is through digitalization and in particular via Digital Twins (DTs) technology. In this …