Choose wisely: An extensive evaluation of model selection for anomaly detection in time series
Anomaly detection is a fundamental task for time-series analytics with important implications
for the downstream performance of many applications. Despite increasing academic interest …
for the downstream performance of many applications. Despite increasing academic interest …
A practical wearable fall detection system based on tiny convolutional neural networks
Falls are a major public health problem in a rapidly aging society due to their high
prevalence and severe consequences among the older population. Therefore, automatic fall …
prevalence and severe consequences among the older population. Therefore, automatic fall …
Hercules against data series similarity search
We propose Hercules, a parallel tree-based technique for exact similarity search on massive
disk-based data series collections. We present novel index construction and query …
disk-based data series collections. We present novel index construction and query …
IEDeaL: a deep learning framework for detecting highly imbalanced interictal epileptiform discharges
Q Wang, S Whitmarsh, V Navarro… - Proceedings of the VLDB …, 2022 - dl.acm.org
Epilepsy is a chronic neurological disease, ranked as the second most burdensome
neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is …
neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is …
ProS: data series progressive k-NN similarity search and classification with probabilistic quality guarantees
Existing systems dealing with the increasing volume of data series cannot guarantee
interactive response times, even for fundamental tasks such as similarity search. Therefore …
interactive response times, even for fundamental tasks such as similarity search. Therefore …
Appliance Detection Using Very Low-Frequency Smart Meter Time Series
A Petralia, P Charpentier, P Boniol… - Proceedings of the 14th …, 2023 - dl.acm.org
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 …
the management of the smart grid system. These meters usually collect energy consumption …
Robust explainer recommendation for time series classification
Time series classification is a task which deals with temporal sequences, a prevalent data
type common in domains such as human activity recognition, sports analytics and general …
type common in domains such as human activity recognition, sports analytics and general …
Evaluating Explanation Methods of Multivariate Time Series Classification through Causal Lenses
E Vareille, A Abbas, M Linardi… - 2023 IEEE 10th …, 2023 - ieeexplore.ieee.org
Explainable machine learning techniques (XAI) aim to provide a solid descriptive approach
to Deep Neural Networks (NN). In Multi-Variate Time Series (MTS) analysis, the most …
to Deep Neural Networks (NN). In Multi-Variate Time Series (MTS) analysis, the most …
Improving the evaluation and actionability of explanation methods for multivariate time series classification
DI Serramazza, TL Nguyen, G Ifrim - Joint European Conference on …, 2024 - Springer
Abstract Explanation for Multivariate Time Series Classification (MTSC) is an important topic
that is under explored. There are very few quantitative evaluation methodologies and even …
that is under explored. There are very few quantitative evaluation methodologies and even …
Evaluating explanation methods for multivariate time series classification
Multivariate time series classification is an important computational task arising in
applications where data is recorded over time and over multiple channels. For example, a …
applications where data is recorded over time and over multiple channels. For example, a …