An autoencoder-based deep learning approach for clustering time series data

This paper introduces a two-stage deep learning-based methodology for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of the given time series data in order to create labels and thus enable transformation of the problem from an un...

Full description

Saved in:

Bibliographic Details
Published in:	SN applied sciences Vol. 2; no. 5; p. 937
Main Authors:	Tavakoli, Neda, Siami-Namini, Sima, Adl Khanghah, Mahdi, Mirza Soltani, Fahimeh, Siami Namin, Akbar
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.05.2020 Springer Nature B.V
Subjects:	Algorithms Applied and Technical Physics Chemistry/Food Science Clustering Data analysis Datasets Deep learning Earth Sciences Engineering Engineering: Application of Machine Learning in Engineering Environment Labels Materials Science Methodology Neural networks Research Article Supervised learning Time series Deep learning Encoder–decoder Time series clustering Supervised learning Kmeans clustering Financial data analysis Multi-layer perceptron Unsupervised learning
ISSN:	2523-3963, 2523-3971
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!