Dynamical Clustering of Time Series Data Using Multi-Decoder RNN AutoencoderDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Withdrawn SubmissionReaders: Everyone
  • Original Pdf: pdf
  • Keywords: Dynamical system, Recurrent neural network, Autoencoder, Variational Bayes, Clustering, Time series data, Driving data
  • TL;DR: Novel time series data clustring algorithm based on dynamical system features.
  • Abstract: Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis. The performance of a clustering algorithm depends on the features extracted from the data. However, in time series analysis, there has been a problem that the conventional methods based on the signal shape are unstable for phase shift, amplitude and signal length variations. In this paper, we propose a new clustering algorithm focused on the dynamical system aspect of the signal using recurrent neural network and variational Bayes method. Our experiments show that our proposed algorithm has a robustness against above variations and boost the classification performance.
  • Code: https://github.com/nosorog3/MDRA
7 Replies