Time series data classification using recurrent neural network with ensemble learning

Authors:
Shinichi Oeda;Ikusaburo Kurimoto;Takumi Ichimura
Affiliations:
Department of Information and Computer Engineering, Kisarazu National College of Technology, Chiba, Japan;Department of Information and Computer Engineering, Kisarazu National College of Technology, Chiba, Japan;Faculty of Information Sciences, Hiroshima City University, Hiroshima, Japan
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2006

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Abstract

In statistics and signal processing, a time series is a sequence of data points, measured typically at successive times, spaced apart at uniform time intervals. Time series prediction is the use of a model to predict future events based on known past events; to predict future data points before they are measured. Solutions in such cases can be provided by non-parametric regression methods, of which each neural network based predictor is a class. As a learning method of time series data with neural network, Elman type Recurrent Neural Network has been known. In this paper, we propose the multi RNN. In order to verify the effectiveness of our proposed method, we experimented by the simple artificial data and the heart pulse wave data.