Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning

DOI

10.2991/jrnal.2018.4.4.1How to use a DOI?

Keywords

deep learning, restricted Boltzmann machine, stochastic gradient ascent, reinforcement learning, error-backpropagation

Abstract

Hinton’s deep auto-encoder (DAE) with multiple restricted Boltzmann machines (RBMs) is trained by the unsupervised learning of RBMs and fine-tuned by the supervised learning with error-backpropagation (BP). Kuremoto et al. proposed a deep belief network (DBN) with RBMs as a time series predictor, and used the same training methods as DAE. Recently, Hirata et al. proposed to fine-tune the DBN with a reinforcement learning (RL) algorithm named “Stochastic Gradient Ascent (SGA)” proposed by Kimura & Kobayashi and showed the priority to the conventional training method by a benchmark time series data CATS. In this paper, DBN with SGA is invested its effectiveness for real time series data. Experiments using atmospheric CO2 concentration, sunspot number, and Darwin sea level pressures were reported.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Takaomi Hirata
AU  - Takashi Kuremoto
AU  - Masanao Obayashi
AU  - Shingo Mabu
AU  - Kunikazu Kobayashi
PY  - 2018
DA  - 2018/03/31
TI  - Forecasting Real Time Series Data using Deep Belief Net and Reinforcement Learning
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 260
EP  - 264
VL  - 4
IS  - 4
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2018.4.4.1
DO  - 10.2991/jrnal.2018.4.4.1
ID  - Hirata2018
ER  -