Training Deep Neural Networks with Reinforcement Learning for Time Series Forecasting

Takashi Kuremoto, Shingo Mabu, Masanao Obayashi, Kunikazu Kobayashi and Takashi Kuremoto

A chapter in Time Series Analysis - Data, Methods, and Applications from IntechOpen

Abstract: As a kind of efficient nonlinear function approximators, artificial neural networks (ANN) have been popularly applied to time series forecasting. The training method of ANN usually utilizes error back-propagation (BP) which is a supervised learning algorithm proposed by Rumelhart et al. in 1986; meanwhile, authors proposed to improve the robustness of the ANN for unknown time series prediction using a reinforcement learning algorithm named stochastic gradient ascent (SGA) originally proposed by Kimura and Kobayashi for control problems in 1998. We also successfully use a deep belief net (DBN) stacked by multiple restricted Boltzmann machines (RBMs) to realized time series forecasting in 2012. In this chapter, a state-of-the-art time series forecasting system that combines RBMs and multilayer perceptron (MLP) and uses SGA training algorithm is introduced. Experiment results showed the high prediction precision of the novel system not only for benchmark data but also for real phenomenon time series data.

Keywords: artificial neural networks (ANN); deep learning (DL); reinforcement learning (RL); deep belief net (DBN); restricted Boltzmann machine (RBM); multilayer perceptron (MLP); stochastic gradient ascent (SGA) (search for similar items in EconPapers)
JEL-codes: C60 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:166187

DOI: 10.5772/intechopen.85457

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