Time series classification is an increasing research topic due to the vast amount of time
series data that is being created over a wide variety of fields. The particularity of the data …

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in
industry. However, based on well over a dozen case studies, we have learned that …

Measured and simulated data sources from the built environment are increasing rapidly. It is
becoming normal to analyze data from hundreds, or even thousands of buildings at once …

There is a need for solutions which assist users to understand long time-series data by
observing its changes over time, finding repeated patterns, detecting outliers, and effectively …

Last decades witness a huge growth in medical applications, genetic analysis, and in
performance of manufacturing technologies and automatised production systems. A …

The problem of anomaly detection in time series has recently received much attention.
However, many existing techniques require the user to provide the length of a potential …

The problems of recurrent and anomalous pattern discovery in time series, eg, motifs and
discords, respectively, have received a lot of attention from researchers in the past decade …

The problem of anomaly detection in time series has received a lot of attention in the past
two decades. However, existing techniques cannot locate where the anomalies are within …

Software developers in big and medium-size companies are working with millions of lines of
code in their codebases. Assuring the quality of this code has shifted from simple defect …

Motif Discovery consists of finding repeated patterns and locating their occurrences in a time
series without prior knowledge about their shape or location. Most state-of-the-art algorithms …