Car Sales Prediction Using Gated Recurrent Units Neural Networks with Reinforcement Learning
In this paper, we propose a novel Gated Recurrent Units neural network with reinforcement learning (GRURL) for car sales forecasting. The car sales time series data usually have a small sample size and appear no periodicity. Many previous time series modeling methods, such as linear regression, cannot effectively obtain the best parameter adjustment strategy when fitting the final prediction values. To cope with this challenge and obtain a higher prediction accuracy, in this paper, we combine the GRU with the reinforcement learning, which can use the reward mechanism to obtain the best parameter adjustment strategy while making a prediction. We carefully investigated a real-world time-series car sales dataset in Yancheng City, Jiangsu Province, and built 140 GRURL models for different car models. Compared with the traditional BP, LSTM, and GRU neural networks, the experimental results show that the proposed GRURL model outperforms these traditional deep neural networks in terms of both prediction accuracy and training cost.
KeywordsCar sales prediction BP LSTM Gated Recurrent Units Reinforcement learning
This research has been supported by the National Natural Science Foundation of China (NSFC) under grants 91846104 and 61603186, the Natural Science Foundation of Jiangsu Province, China, under grants BK20160843, and the China Postdoctoral Science Foundation under grants 2017T100370.
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