Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

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 …

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 …

Structural identity is a concept of symmetry in which network nodes are identified according
to the network structure and their relationship to other nodes. Structural identity has been …

Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

Time series classification is an important task in time series data mining, and has attracted
great interests and tremendous efforts during last decades. However, it remains a …

In the last 5 years there have been a large number of new time series classification
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …

Recently, big data streams have become ubiquitous due to the fact that a number of
applications generate a huge amount of data at a great velocity. This made it difficult for …

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 motifs have been in the literature for about fifteen years, but have only recently
begun to receive significant attention in the research community. This is perhaps due to the …