Research in computer analysis of medical images bears many promises to improve patients'
health. However, a number of systematic challenges are slowing down the progress of the …

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

Rocket achieves state-of-the-art accuracy for time series classification with a fraction of the
computational expense of most existing methods by transforming input time series using …

This paper brings deep learning at the forefront of research into time series classification
(TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of …

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 …

Abstract The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE)
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …

Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …

The UCR time series archive–introduced in 2002, has become an important resource in the
time series data mining community, with at least one thousand published papers making use …

In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …

How to accurately predict unknown quality-of-service (QoS) data based on observed ones is
a hot yet thorny issue in Web service-related applications. Recently, a latent factor (LF) …