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 …

Detecting anomalies in time series data is important in a variety of fields, including system
monitoring, healthcare and cybersecurity. While the abundance of available methods makes …

Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-
related data providing accessible knowledge on the actual performance of buildings during …

As the core power transmission component of mechanical equipment, planetary gearboxes
unavoidably experience faults due to harsh working environments and continuously heavy …

Abstract The Matrix Profile is a state-of-the-art time series analysis technique that can be
used for motif discovery, anomaly detection, segmentation and others, in various domains …

Companies are increasingly gathering and analyzing time-series data, driven by the rising
number of IoT devices. Many works in literature describe analysis systems built using either …

The matrix profile (MP) is a data structure computed from a time series which encodes the
data required to locate motifs and discords, corresponding to recurring patterns and outliers …

Increased adoption of energy monitoring devices across the energy system has resulted in
large quantities of multivariate measurement data sets available for analysis at multi-scale …

As companies rely on an ever increasing number of connected devices for their day to day
operations, a need arises for automated anomaly detectors to constantly observe crucial …

Time series analysis is becoming more popular in both research and industry. One recent
innovation is the Ostinato algorithm, which finds the best preserved patterns that are …