Vortrag von Dr. Patrick Schäfer: "Unleashing the Potential of Unsupervised Time Series Analytics: Recent Advances and Breakthroughs"
31. März 2023 - 10:30 Uhr
Dr. Patrick Schäfer, wissenschaftlicher Mitarbeiter und Dozent für Informatik an der Humboldt Universität Berlin.
Zu seinen Hauptforschungsinteressen gehören Scalable TS Analytics, einschließlich Supervised Tasks wie Classification und Unsupervised Tasks wie Motif Discovery und Segmentation. Er ist Mitglied des Editorial Boards des DAMI Journals und Hauptentwickler von sktime.
In recent years, the usage of low-cost, high-resolution sensors has witnessed a surge, with applications in various domains, such as mobile devices, manufacturing monitoring, environmental and medical surveillance, and more. These sensors produce vast amounts of unlabeled, real-valued sequences, also referred to as time series (TS). Despite the abundance of data, the research has primarily focused on supervised techniques for analyzing TS, with a focus on classification and deep learning methods.
However, the field of unsupervised time series analytics (UTSA) offers a broad spectrum of tools to extract valuable information from time series data. This includes primitives like Chains, Discords, Motifs, or Change Points. These enable us to gain insights into the inherent statistical properties and temporal patterns of the underlying processes and enhance our understanding of the generated data. In this talk, I will present our recent advances in UTSA, specifically the detection of approximately repeated frequent patterns (Motiflets) and segmentation (ClaSP, ClaSS).
Gebäude 50.34, Raum 301