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 …

Over the past decade, multivariate time series classification has received great attention. We
propose transforming the existing univariate time series classification models, the Long …

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 …

Time series data usually contains local and global patterns. Most of the existing feature
networks focus on local features rather than the relationships among them. The latter is also …

Multivariate Time Series (MTS) classification has gained importance over the past decade
with the increase in the number of temporal datasets in multiple domains. The current state …

Multivariate time series (MTS) arise when multiple interconnected sensors record data over
time. Dealing with this high-dimensional data is challenging for every classifier for at least …

Shapelets are discriminative subsequences of time series, usually embedded in shapelet-
based decision trees. The enumeration of time series shapelets is, however, computationally …

Time series classification has attracted much attention in the last two decades. However, in
many real-world applications, the acquisition of sufficient amounts of labeled training data is …

Time series has been a popular research topic over the past decade. Salient subsequences
of time series that can benefit the learning task, eg, classification or clustering, are called …

In responding to freight transportation fire incidents, first responders refer to the terials
labeled on the freights and the Emergency Response Guidebook (ERG) for guidance on the …