Paul Boniol
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View article: Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures
Advances in Time-Series Anomaly Detection: Algorithms, Benchmarks, and Evaluation Measures Open
International audience
View article: DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series
DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series Open
In recent years, electricity suppliers have installed millions of smart meters worldwide to improve the management of the smart grid system. These meters collect a large amount of electrical consumption data to produce valuable information…
View article: Few Labels are All you Need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
Few Labels are All you Need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series Open
Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for electricity suppliers. In this regard, millions of smart met…
View article: Graphint: Graph-based Time Series Clustering Visualisation Tool
Graphint: Graph-based Time Series Clustering Visualisation Tool Open
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 patterns in these datasets. However, prevailing methods ofte…
View article: Time Series Motif Discovery: A Comprehensive Evaluation
Time Series Motif Discovery: A Comprehensive Evaluation Open
Motif Discovery involves identifying recurring patterns and locating their occurrences within a time series without prior knowledge about their shape or location. In practice, Motif Discovery faces several data-related challenges, leading …
View article: $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering Open
International audience
View article: $k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering Open
Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with centroids. In address…
View article: VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection Open
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 where AD mainly focuses on point-based anomalies (i.e., ou…
View article: Dive into Time-Series Anomaly Detection: A Decade Review
Dive into Time-Series Anomaly Detection: A Decade Review Open
Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important a…
View article: Time-Series Anomaly Detection: Overview and New Trends
Time-Series Anomaly Detection: Overview and New Trends Open
Anomaly detection is a fundamental data analytics task across scientific fields and industries. In recent years, an increasing interest has been shown in the application of anomaly detection techniques to time series. In this tutorial, we …
View article: d symb Playground: An Interactive Tool to Explore Large Multivariate Time Series Datasets
d symb Playground: An Interactive Tool to Explore Large Multivariate Time Series Datasets Open
International audience
View article: Arm-CODA: A Data Set of Upper-limb Human Movement During Routine Examination
Arm-CODA: A Data Set of Upper-limb Human Movement During Routine Examination Open
International audience
View article: Appliance Detection Using Very Low-Frequency Smart Meter Time Series
Appliance Detection Using Very Low-Frequency Smart Meter Time Series Open
In recent years, smart meters have been widely adopted by electricity\nsuppliers to improve the management of the smart grid system. These meters\nusually collect energy consumption data at a very low frequency (every 30min),\nenabling uti…
View article: dCNN/dCAM: anomaly precursors discovery in multivariate time series with deep convolutional neural networks
dCNN/dCAM: anomaly precursors discovery in multivariate time series with deep convolutional neural networks Open
Detection of defects and identification of symptoms in monitoring industrial systems is a widely studied problem with applications in a wide range of domains. Most of the monitored information extracted from systems corresponds to data ser…
View article: dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification Open
Data series classification is an important and challenging problem in data\nscience. Explaining the classification decisions by finding the discriminant\nparts of the input that led the algorithm to some decisions is a real need in\nmany a…
View article: Series2Graph
Series2Graph Open
Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches that have been proposed so far in the literature have severe limitations: they either require pri…