99 citations · 149 across the 9 of their papers we have counts for
12 papers
Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection
Christos Panourgias, Roberto Stanzione, Adrien Petralia +2
Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications. Despite the large number of anom…
Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability
Roberto Stanzione, Jules Barbe, Magali Parrino +2
Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led to the development of numerous d…
What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series
Magali Parrino, Antoine Ajenjo, Emmanuel Remy +2
EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streaming method in unsupervised setti…
In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
Magali Parrino, Antoine Ajenjo, Emmanuel Remy +3
Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works fro…
MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection
Emmanouil Sylligardos, John Paparrizos, Themis Palpanas +2
Anomaly detection is a fundamental task for time series analytics with important implications for the downstream performance of many applications. Despite increasing academic inter…
Graphint: Graph-based Time Series Clustering Visualisation Tool
Paul Boniol, Donato Tiano, Angela Bonifati +1
With the exponential growth of time series data across diverse domains, there is a pressing need for effective analysis tools. Time series clustering is important for identifying p…
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection
Paul Boniol, Ashwin K. Krishna, Marine Bruel +7
Anomaly detection (AD) is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains…
-Graph: A Graph Embedding for Interpretable Time Series Clustering
Paul Boniol, Donato Tiano, Angela Bonifati +1
Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, of…
Appliance Detection Using Very Low-Frequency Smart Meter Time Series
Adrien Petralia, Philippe Charpentier, Paul Boniol +1
In recent years, smart meters have been widely adopted by electricity suppliers to improve the management of the smart grid system. These meters usually collect energy consumption…
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series
Paul Boniol, Themis Palpanas
Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches proposed so far in the literature have…
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
Paul Boniol, Mohammed Meftah, Emmanuel Remy +1
Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led…
Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
Paul Boniol, George Panagopoulos, Christos Xypolopoulos +3
Artificial Intelligence techniques are already popular and important in the legal domain. We extract legal indicators from judicial judgment to decrease the asymmetry of informatio…