Anomaly detection is a fundamental task for time-series analytics with important implications
for the downstream performance of many applications. Despite increasing academic interest …

Anomaly detection is a fundamental data analytics task across scientific fields and
industries. In recent years, an increasing interest has been shown in the application of …

Standard k-means clustering necessitates computing pairwise Euclidean distances between
each instance x in a data set D and all cluster centers, resulting in inadequate efficiency …

Time series data analysis algorithms have been gaining significant importance in the
research community. Extensive studies have confirmed that Dynamic Time Warping (DTW) …

Clustering is one of the most popular time-series tasks because it enables unsupervised
data exploration and often serves as a subroutine or preprocessing step for other tasks …

Distance measures are core building blocks in time-series analysis and the subject of active
research for decades. Unfortunately, the most detailed experimental study in this area is …

Time-series anomaly detection is a fundamental task across scientific fields and industries.
However, the field has long faced the``elephant in the room:''critical issues including flawed …

The ubiquity sensory devices across scientific and industrial settings have made time-series
data mining an important research topic in the IoT domain. Among all time-series mining …

The proliferation of IoT devices generates vast amounts of sensor data, making sensor data
mining an increasingly important research topic in the IoT domain. The Top-k similarity …

A variety of distance measures for multivariate time series has been proposed in recent
literature. However, evaluations of such measures have been incomplete; comparisons are …