Research on price prediction of natural gas futures using LSTM neural network

Jiarong Mu; Xianfeng Liu; Peiyan Zang; Shuming Li

doi:10.1117/12.2626476

21 December 2021 Research on price prediction of natural gas futures using LSTM neural network

Jiarong Mu, Xianfeng Liu, Peiyan Zang, Shuming Li

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 1215611 (2021) https://doi.org/10.1117/12.2626476
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

Abstract

With the rapid advancement of natural gas industry in the world, governments are paying close attention to the development of clean energy such as natural gas. The government of China also actively promotes the market-oriented reform of natural gas price. The growth rate of natural gas production in China is far less than that of natural gas consumption, which poses a threat to energy security. China is under the situation of “rich coal but short of oil and gas”.The price is a signal of market regulation, and the effective prediction of Henry Hub gas futures price can stimulate the enthusiasm of the producers to a certain extent, and also can effectively formulate a complete price strategy. Therefore, in view of the non-linear and long-term dependence of the natural gas futures price, this paper makes a prediction through the long-term and short-term memory (LSTM) model, which can be extended in time and has long-term memory function, the gradient explosion problem of traditional neural network is solved effectively. This paper selects the Henry hub 2010 -2020 natural gas futures price data in North America as an empirical study sample. The results show that the LSTM neural network has a certain practical significance and a wonderful effect in predicting the price of natural gas futures.

Citation Download Citation

Jiarong Mu, Xianfeng Liu, Peiyan Zang, and Shuming Li "Research on price prediction of natural gas futures using LSTM neural network", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 1215611 (21 December 2021); https://doi.org/10.1117/12.2626476

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