Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …

Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

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 …

Deep learning architectures usually require large scale labeled datasets for achieving good
performance on general classification tasks including computer vision and natural language …

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

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 …

This paper presents the first time series clustering benchmark utilizing all time series
datasets currently available in the University of California Riverside (UCR) archive—the …