Multivariate time series (MTS) classification is a challenging problem due to the complex
nature of data, especially for tasks with spatial dependencies such as three-dimensional …

In recent years, deep learning approaches have shown remarkable advancements in
Multivariate Time Series Classification (MTSC) tasks. However, the existing approaches …

Deep learning-based algorithms, eg, convolutional networks, have significantly facilitated
multivariate time series classification (MTSC) task. Nevertheless, they suffer from the …

Multivariate time series classification is one of the increasingly important issues in machine
learning. Existing methods focus on establishing the global long-range dependencies or …

Multivariate time series (MTSs) are widely found in many important application fields, for
example, medicine, multimedia, manufacturing, action recognition, and speech recognition …

Deep learning has revolutionized many areas, including time series data mining.
Multivariate time series classification (MTSC) remained to be a well-known problem in the …

Contrastive learning, as a self-supervised learning paradigm, becomes popular for
Multivariate Time-Series (MTS) classification. It ensures the consistency across different …

Multivariate time series classification (MTSC) is a crucial task in data science, providing a
foundation for analyzing and predicting complex, multi-dimensional data patterns. However …

Multivariate time series classification (MTSC) is a crucial data mining task that can be
effectively tackled using prevalent deep learning technology. However, current methods …

Multivariate time series classification (MTSC) is a fundamental and essential research
problem in the domain of time series data mining. Recently deep neural networks emerged …