Multivariate time series classification is a machine learning problem that can be applied to
automate a wide range of real-world data analysis tasks. RandOm Convolutional KErnel …

This paper investigates different methods and various neural network architectures
applicable in the time series classification domain. The data is obtained from a fleet of gas …

Nowadays, with the rising number of sensor signals in sectors such as healthcare and
industry, the problem of multivariate time series classification (MTSC) is getting increasingly …

Time series classification is related to many different domains, such as health informatics,
finance, and bioinformatics. Due to its broad applications, researchers have developed …

Most methods for time series classification that attain state-of-the-art accuracy have high
computational complexity, requiring significant training time even for smaller datasets, and …

This paper presents a novel, generic, scalable, autonomous, and flexible supervised
learning algorithm for the classification of multi-variate and variable length time series. The …

The UEA Multivariate Time Series Classification (MTSC) archive released in 2018 provides
an opportunity to evaluate many existing time series classifiers on the MTSC task …

Time Series Classification (TSC) involved building predictive models for a discrete target
variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms …

Time series classification is an important research topic in machine learning and data
mining communities, since time series data exist in many application domains. Recent …

Deep learning-based time series classification techniques have significantly improved in
recent years. While previous works have mentioned the fundamental importance of temporal …