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 …

Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

We present a novel framework for multivariate time series representation learning based on
the transformer encoder architecture. The framework includes an unsupervised pre-training …

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 …

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 …

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 …

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 …

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

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 …

Recently, contrastive learning (CL) is a promising way of learning discriminative
representations from time series data. In the representation hierarchy, semantic information …