9 citations · 10 across the 6 of their papers we have counts for
6 papers
Motif Discovery Framework for Psychiatric EEG Data Classification
Melanija Kraljevska, Katerina Hlavackova-Schindler, Lukas Miklautz +1
In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable ef…
Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs
Alexander Bakumenko, Kateřina Hlaváčková-Schindler, Claudia Plant +1
Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algor…
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length
Katerina Hlavackova-Schindler, Anna Melnykova, Irene Tubikanec
Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used to model various real-life phenomena: earthquakes, operations on stock markets, neuronal activity, virus…
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree
Christina Pacher, Irene Schicker, Rosmarie deWit +2
Both clustering and outlier detection play an important role for meteorological measurements. We present the AWT algorithm, a clustering algorithm for time series data that also pe…
Causal Discovery in Hawkes Processes by Minimum Description Length
Amirkasra Jalaldoust, Katerina Hlavackova-Schindler, Claudia Plant
Hawkes processes are a special class of temporal point processes which exhibit a natural notion of causality, as occurrence of events in the past may increase the probability of ev…
Interpretable Gait Recognition by Granger Causality
Michal Balazia, Katerina Hlavackova-Schindler, Petr Sojka +1
Which joint interactions in the human gait cycle can be used as biometric characteristics? Most current methods on gait recognition suffer from the lack of interpretability. We pro…