This article proposes a dual-network-based feature extractor, perceptive capsule network
(PCapN), for multivariate time series classification (MTSC), including a local feature network …

Multivariate time series classification is widely available in several areas of real life and has
attracted the attention of many researchers. In recent years, many multivariate time series …

In various areas of real life, Multivariate Time Series Classification (MTSC) is widely used. It
has been the focus of attention of many researchers, and a number of MTSC methods have …

Multivariate time series (MTS), whose patterns change dynamically, often have complex
temporal and dimensional dependence. Most existing reconstruction-based MTS anomaly …

In recent years, time series classification methods based on shapelet features have attracted
significant research interest because they are interpretable. Although researchers have …

Time series data are ubiquitous in human society and nature, and classification is one of the
most significant problems in the field of time series mining. Although it has been intensively …

While existing reconstruction-based multivariate time series (MTS) anomaly detection
methods demonstrate advanced performance on many challenging real-world datasets, they …

High labor costs and the requirement for significant domain expertise often result in a lack of
anomaly labels in most time series. Consequently, employing unsupervised methods …

Biopolymer-bound soil composites (BSC) are a novel class of cement-free building materials
utilizing starch, protein, and lignin binders. While BSC are sustainable composite materials …

Various deep learning architectures have been developed to capture long-term
dependencies in time series data, but challenges such as overfitting and computational time …