The geometry of dynamical systems estimated from trajectory data is a major chal- lenge for machine learning applications. Koopman and transfer operators provide a linear …
Motif Discovery aims at identifying repeated patterns in time series. It is a fundamental task in time series analysis, with applications across numerous fields where recurring …
The geometry of dynamical systems estimated from trajectory data is a major challenge for machine learning applications. Koopman and transfer operators provide a linear …
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.
Automatically extracting robust representations from large and complex time series data is becoming imperative for several real-world applications. Unfortunately, the potential of …
Analyzing inter-individual variability of physiological functions is particularly appealing in medical and biological contexts to describe or quantify health conditions. Such …
There is a growing concern about credibility and trustworthiness in results and claims of research in computational data science, and at the same time, difficulty in taking those …
Finding repeating or anomalous subsequences in long time series is a crucial task in numerous data analysis pipelines. Most of those methods share a common step where they compute …
Time series analysis based on recurrent patterns, also called motifs, has emerged as a powerful approach in various domains. However, uncovering recurrent patterns poses challenges …
We present a new algorithm for pattern detection in time series. It uses a graph that quantifies the similarity of pairs of subsequences of a given signal, and topological data …