Author details

Takashi Kuremoto<sup>1</sup> \*, Takaomi Hirata<sup>1</sup> , Masanao Obayashi<sup>1</sup> , Shingo Mabu1 and Kunikazu Kobayashi<sup>2</sup>

1 Yamaguchi University, Ube, Japan

2 Aichi Prefectural University, Nagakute, Japan

\*Address all correspondence to: wu@yamaguchi-u.ac.jp

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

learning process, i.e., different from the BP method which adopts all errors of every sample to modify the model. Additionally, the optimization of the structure of DBN was realized by random search method. Time series forecasting experiments used

Prediction results of natural phenomenon time series data of TSDL. (a) Prediction result of Lynx; (b) prediction result of sunspots; (c) prediction result of river flow; (d) prediction result of vehicles; (e) prediction

result of RGNP; (f) prediction result of wine; and (g) prediction result of airline.

Time Series Analysis - Data, Methods, and Applications

Figure 11.

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