Early classification of time series has been extensively studied for minimizing class
prediction delay in time-sensitive applications such as medical diagnostic and industrial …

This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …

Over the years, a number of semisupervised deep‐learning algorithms have been proposed
for time‐series classification (TSC). In semisupervised deep learning, from the point of view …

Over the past decade, multivariate time series classification has received great attention. We
propose transforming the existing univariate time series classification models, the Long …

Time series data usually contains local and global patterns. Most of the existing feature
networks focus on local features rather than the relationships among them. The latter is also …

Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …

Wind energy is of great importance for future energy development. In order to fully exploit
wind energy, wind farms are often located at high latitudes, a practice that is accompanied …

Wind farms are usually located in high-latitude areas, which bring a high risk of icing.
Traditional methods of anti-blade-icing are limited by extra costs and potential damages to …

Multivariate time series (MTS) classification is an emerging field with increasing demand.
Existing representation learning methods for MTS classification are generally based on self …

The problem of early classification of time series appears naturally in contexts where the
data, of temporal nature, are collected over time, and early class predictions are interesting …