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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Time Series Data Classification Using Recurrent Neural Network with Ensemble Learning
verfasst von : Shinichi Oeda, Ikusaburo Kurimoto, Takumi Ichimura
Erschienen in: Knowledge-Based Intelligent Information and Engineering Systems
Verlag: Springer Berlin Heidelberg
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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.