id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-024494-i6puqauk	Ienco, Dino	Deep Multivariate Time Series Embedding Clustering via Attentive-Gated Autoencoder	2020-04-17		.txt	text/plain	3863	240	54	In this paper we propose a deep-learning based framework for clustering multivariate time series data with varying lengths. Our framework, namely DeTSEC (Deep Time Series Embedding Clustering), includes two stages: firstly a recurrent autoencoder exploits attention and gating mechanisms to produce a preliminary embedding representation; then, a clustering refinement stage is introduced to stretch the embedding manifold towards the corresponding clusters. In this work, we propose a new deep-learning based framework, namely DeT-SEC (Deep Time Series Embedding Clustering), to cope with multivariate timeseries clustering. In the first one, the GRU based autoencoder is exploited to summarize the time-series information and to produce the new vector embedding representation, obtained by forcing the network to reconstruct the original signal, that integrates the temporal behavior and the multi-dimensional information. In this paper we have presented DeTSEC, a deep learning based approach to cluster multivariate time series data of variable length.	./cache/cord-024494-i6puqauk.txt	./txt/cord-024494-i6puqauk.txt