Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …

This paper presents an empirical study of feature extraction methods for the application of
low-speed slew bearing condition monitoring. The aim of the study is to find the proper …

The UCR time series archive–introduced in 2002, has become an important resource in the
time series data mining community, with at least one thousand published papers making use …

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 …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

The massive data generated by the Internet of Things (IoT) are considered of high business
value, and data mining algorithms can be applied to IoT to extract hidden information from …

Similarity search is one of the most important and probably best studied methods for data
mining. In the context of time series analysis it reaches its limits when it comes to mining raw …

Time series clustering is an active research area with applications in a wide range of fields.
One key component in cluster analysis is determining a proper dissimilarity measure …

In almost every scientific field, measurements are performed over time. These observations
lead to a collection of organized data called time series. The purpose of time-series data …

Sequences of feature vectors are a natural way of representing temporal data. Given a
database of sequences, a fundamental task is to find the database entry which is the most …