Autoencoder-Enhanced Clustering: A Dimensionality Reduction Approach to Financial Time Series
Author:
Affiliation:
1. NEOMA Business School, Mont-Saint-Aignan, France
2. Faculty of Engineering, University of Deusto, Bilbao, Spain
Funder
NEOMA Business School
Data Science for Insight and Value Creation, Research Group of the AE AI, Data Science and Business, NEOMA Business School
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10415421.pdf?arnumber=10415421
Reference72 articles.
1. Breaking the curse of dimensionality: hierarchical Bayesian network model for multi-view clustering
2. An improved clustering algorithm and its application in IoT data analysis
3. Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series
4. Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories
5. A survey on ECG analysis
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