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 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 …

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 …

Time-series data contains temporal order information that can guide representation learning
for predictive end tasks (eg, classification, regression). Recently, there are some attempts to …

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

Deep learning models developed for time-series associated tasks have become more
widely researched nowadays. However, due to the unintuitive nature of time-series data, the …

Abstract Dynamic Time Warping (DTW) is widely used for temporal data processing.
However, existing methods can neither learn the discriminative prototypes of different …

Classification models for multivariate time series have drawn the interest of many
researchers to the field with the objective of developing accurate and efficient models …

With the increase of available time series data, predicting their class labels has been one of
the most important challenges in a wide range of disciplines. Recent studies on time series …

Semi-supervised time-series classification could effectively alleviate the issue of lacking
labeled data. However, existing approaches usually ignore model interpretability, making it …