Forecast chaotic time series data by DBNs
Takashi Kuremoto, Masanao Obayashi, Kunikazu Kobayashi, Takaomi Hirata, Shingo Mabu · 2014
Deep belief nets (DBNs) with multiple artificial neural networks (ANNs) have attracted many researchers recently. In this paper, we propose to compose restricted Boltzmann machine (RBM) and multi-layer perceptron (MLP) as a DBN to predict chaotic time series data, such as the Lorenz chaos and the Henon map. Experiment results showed that in the sense of prediction precision, the novel DBN performed better than the conventional DBN with RBMs.