Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Most of state of the art methods applied on time series consist of deep learning methods that
are too complex to be interpreted. This lack of interpretability is a major drawback, as several …

Time series shapelets are short discriminative subsequences that recently have been found
not only to be accurate but also interpretable for the classification problem of univariate time …

Enhancing the expressive capacity of deep learning-based time series models with self-
supervised pre-training has become ever-increasingly prevalent in time series classification …

Along with the widespread application of Internet of things technology, time series
classification have been becoming a research hotspot in the field of data mining for massive …

Energy plays a pivotal role for economic development of a country. A reliable energy source
is needed to improve the living standards of people. To achieve such a goal, governments …

In recent years, time series classification methods based on shapelet features have attracted
significant research interest because they are interpretable. Although researchers have …

The collection of large volumes of temporal data during the production process is
streamlined in a cyber manufacturing environment. The ineluctable abnormal patterns in …

Early classification predicts the class of the incoming sequences before it is completely
observed. How to quickly classify streaming time series without losing interpretability …