Abstract: For multivariate time series classification, current research predominantly focuses on contrastive learning to acquire suitable representations. Despite their successes in enhancing accuracy ...

Opinions expressed by Digital Journal contributors are their own. “As an inventor, I’ve always believed that data holds the key to solving the most complex problems. My goal has been to create ...

Abstract: With the development of sensor technology, multi-variate time series classification is an essential element in time data mining. Multivariate time series are everywhere in our daily lives, ...

While there are a number of advanced data analysis techniques that allow us to embrace distributed electrophysiological activity measured by MEG, these tools are somewhat underexploited. This includes ...

Multivariate Time Series Classification (MTSC) is one of most important tasks in time series analysis, aiding in activities such as human motion recognition and medical diagnostics. Existing methods ...

Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that ...

Irregular multivariate time series (IMTS) is characterized by the lack of synchronized observations across its different channels. In this paper, we point out that this channel-wise asynchrony can ...