Transformer-based deep learning methods have significantly facilitated multivariate time
series classification (MTSC) tasks. However, due to the inherent operation of self-attention …

In this paper, considering the infinite memory capability of fractional-order differential
equations and the advantages of broad echo state network, a broad fractional-order echo …

In order to achieve sustainable development of marine fishery resources, effective supervise
of trawl fishing during forbidden fishing period is of great significance. This paper addresses …

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 …

Urban public open spaces (POS) are pivotal in sustainable urban planning, recognized for
their positive impacts on the health of residents and environments. However, understanding …

Multivariate time series classification is of great importance in practical applications and is a
challenging task. However, deep neural network models such as Transformers exhibit high …

Time series classification assigns predefined labels or classes to sequences of data points
ordered chronologically, which is a fundamental task for time series analysis. Existing time …

Multivariate time series classification is a significant research topic in the realm of data
mining, which encompasses a wide array of practical applications in domains such as …

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 …

In time series classification tasks, most datasets have small data volumes or are
inconvenient to collect. Therefore, we proposed a data augmentation framework based on …