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 …

The information technology revolution, especially with the adoption of the Internet of Things,
longitudinal data in many domains become more available and accessible for secondary …

Early classification of time series aims to predict the class value of a sequence accurately as
early as possible, not wait for the full-length data, which is significant in many time-sensitive …

Large fleets of industrial and construction vehicles require periodic maintenance activities.
Scheduling these operations is potentially challenging because the optimal timeline …

The volume of time series data has exploded due to the popularity of new applications, such
as data center management and IoT. Subsequence matching is a fundamental task in …

Our current understanding of brain rhythms is based on quantifying their instantaneous or
time-averaged characteristics. What remains unexplored is the actual structure of the waves …

Sequence feature embedding is a challenging task due to the unstructuredness of
sequences—arbitrary strings of arbitrary length. Existing methods are efficient in extracting …

Time series analysis is a field of data science which is interested in analyzing sequences of
numerical values ordered in time. Time series are particularly interesting because they allow …

Processing multivariate sensory time-series with variable lengths is a challenging problem
across different application domains due to the naturally complex, high-dimensional, and …

Multi-phased time series are found in many industrial processes. Their classification still
poses a big challenge for algorithms compared to single-phased time series forms. To …