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 …
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 …
The study of plethysmography time series is crucial to better understand the breathing behavior of mice, in particular the influence of neurotoxins on the respiratory system. …
Plethysmography groups together a set of non-invasive methods for measuring respiratory volumes. The current techniques of analysis of the obtained signals focus on the extraction …