Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 151, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113378
Keywords
Time series classification; Anomaly detection; Feature extraction; Deep neural networks; Machine learning; Big data analytics; Apache spark; Hadoop
Categories
Funding
- COST (European Cooperation in Science and Technology)
- Ministry of Education, Universities and Research (MIUR) through the project ComESto Community Energy Storage: Gestione Aggregata di Sistemi dAccumulo dellEnergia in Power Cloud [ARS01_01259]
- Ministry of Education, Universities and Research (MIUR) through PON Ricerca e Innovazione 2014-2020 project CLOSE -Close to the Earth - Italian Ministry for Universities and Research (MIUR) [ARS01_001413]
- Faculty of Computer Science and Engineering
- Ss. Cyril
- Methodius University
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Gravitational waves represent a new opportunity to study and interpret phenomena from the universe. In order to efficiently detect and analyze them, advanced and automatic signal processing and machine learning techniques could help to support standard tools and techniques. Another challenge relates to the large volume of data collected by the detectors on a daily basis, which creates a gap between the amount of data generated and effectively analyzed. In this paper, we propose two approaches involving deep auto-encoder models to analyze time series collected from Gravitational Waves detectors and provide a classification label (noise or real signal). The purpose is to discard noisy time series accurately and identify time series that potentially contain a real phenomenon. Experiments carried out on three datasets show that the proposed approaches implemented using the Apache Spark framework, represent a valuable machine learning tool for astrophysical analysis, offering competitive accuracy and scalability performances with respect to state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved.
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