In the last decade unsupervised pattern discovery in time series, ie the problem of finding
recurrent similar subsequences in long multivariate time series without the need of querying …

In dem letzten Jahrzehnt hat die unüberwachte Mustererkennung in Zeitreihen–die
Identifikation von ähnlichen, wiederkehrenden, im Vorfeld nicht bekannten Teilsequenzen in …

A company from the automotive sector has shown interest in looking for cause-effect
relations in some systems with multiple input and output signals. The available data from …

While analyzing vehicular sensor data, we found that frequently occurring waveforms could
serve as features for further analysis, such as rule mining, classification, and anomaly …

In technical systems the analysis of similar situations is a promising technique to gain
information about the system's state, its health or wearing. Very often, situations cannot be …

We present a new algorithm for discovering patterns in time series and other sequential
data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian …

In the present-day, sensor data and textual logs are generated by many devices. Analysing
these time series data leads to the discovery of interesting patterns and anomalies. In recent …

Machine learning tasks typically require large amount of data for training. This dissertation
focuses on time series analysis, which is a frequent type of data collected in industry. The …

Time series is perhaps the most commonly encountered data type, touching almost every
aspect of human life, including medicine (ECG, EEG data), finance (stock market data, credit …

In many applications that track and analyze spatiotemporal data, movements obey periodic
patterns; the objects follow the same routes (approximately) over regular time intervals. For …