Multi-Decoder RNN Autoencoder Based on Variational Bayes Method

2020/04/29 by Daisuke Kaji, Kazuho Watanabe, Kaji, Daisuke +3
Computer Science · #Anomaly Detection Techniques and Applications #Applications (stat.AP) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.2004.14016

openalex publication_date 2020/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis. However, in time series analysis, most of the algorithms used signal shape features or the initial value of hidden variable of a neural network. Little has been discussed on the methods based on the generative model of the time series. In this paper, we propose a new clustering algorithm focusing on the generative process of the signal with a recurrent neural network and the variational Bayes method. Our experiments show that the proposed algorithm not only has a robustness against for phase shift, amplitude and signal length variations but also provide a flexible clustering based on the property of the variational Bayes method.

Citations

• On the difficulty of training Recurrent Neural Networks
• Auto-Encoding Variational Bayes
• Long Short-Term Memory
• Finding Structure in Time
• Learning to Forget: Continual Prediction with LSTM
• Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs

Related

• Variational Recurrent Auto-Encoders
• Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering
• Note on Equivalence Between Recurrent Neural Network Time Series Models\n and Variational Bayesian Models
• Time Series Clustering with General State Space Models via Stochastic Variational Inference
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder