Forecast chaotic time series data by DBNs

Author

Kuremoto, Takashi ; Obayashi, Masanao ; Kobayashi, Kaoru ; Hirata, Takaomi ; Mabu, Shingo

Author_Institution

Grad. Sch. of Sci. & Eng., Yamaguchi Univ., Ube, Japan

fYear

2014

fDate

14-16 Oct. 2014

Firstpage

1130

Lastpage

1135

Abstract

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.

Keywords

Boltzmann machines; Henon mapping; chaos; data analysis; multilayer perceptrons; time series; DBN; Henon map; Lorenz chaos; MLP; RBM; chaotic time series data forecasting; chaotic time series data prediction; deep belief networks; multilayer perceptron; restricted Boltzmann machine; Artificial neural networks; Chaos; Educational institutions; Feature extraction; Forecasting; Predictive models; Time series analysis; Deep Belief Net; Deep learning; chaos; forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Image and Signal Processing (CISP), 2014 7th International Congress on

Conference_Location

Dalian

Type

conf

DOI

10.1109/CISP.2014.7003950

Filename

7003950

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1791473