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 …

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 …

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 …

Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art
performance on the task of classifying time series sequences. We propose the augmentation …

With the advance of sensor technologies, the Multivariate Time Series classification (MTSC)
problem, perhaps one of the most essential problems in the time series data mining domain …

Abstract Time Series Classification (TSC) has seen enormous progress over the last two
decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is …

Time series classification (TSC) is home to a number of algorithm groups that utilise different
kinds of discriminatory patterns. One of these groups describes classifiers that predict using …

Long short-term memory fully convolutional neural networks (LSTM-FCNs) and Attention
LSTM-FCN (ALSTM-FCN) have shown to achieve the state-of-the-art performance on the …