Forecasting crude oil price using LSTM neural networks

Kexian Zhang; Min Hong; Kexian Zhang; Min Hong

doi:10.3934/DSFE.2022008

Data Science in Finance and Economics

2022, Volume 2, Issue 3: 163-180. doi: 10.3934/DSFE.2022008

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Forecasting crude oil price using LSTM neural networks

Kexian Zhang ^,,
Min Hong

School of Economics and Management, Hunan Institute of Technology, Hengyang 421002, China

Received: 20 February 2022 Revised: 09 May 2022 Accepted: 18 May 2022 Published: 07 July 2022
JEL Codes: C53, C81, C82, E37

As a key input factor in industrial production, the price volatility of crude oil often brings about economic volatility, so forecasting crude oil price has always been a pivotal issue in economics. In our study, we constructed an LSTM (short for Long Short-Term Memory neural network) model to conduct this forecasting based on data from February 1986 to May 2021. An ANN (short for Artificial Neural Network) model and a typical ARIMA (short for Autoregressive Integrated Moving Average) model are taken as the comparable models. The results show that, first, the LSTM model has strong generalization ability, with stable applicability in forecasting crude oil prices with different timescales. Second, as compared to other models, the LSTM model generally has higher forecasting accuracy for crude oil prices with different timescales. Third, an LSTM model-derived shorter forecast price timescale corresponds to a lower forecasting accuracy. Therefore, given a longer forecast crude oil price timescale, other factors may need to be included in the model.
- crude oil price,
- forecast,
- LSTM neural network model,
- artificial neural networks,
- ARIMA
Citation: Kexian Zhang, Min Hong. Forecasting crude oil price using LSTM neural networks[J]. Data Science in Finance and Economics, 2022, 2(3): 163-180. doi: 10.3934/DSFE.2022008

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Abstract

As a key input factor in industrial production, the price volatility of crude oil often brings about economic volatility, so forecasting crude oil price has always been a pivotal issue in economics. In our study, we constructed an LSTM (short for Long Short-Term Memory neural network) model to conduct this forecasting based on data from February 1986 to May 2021. An ANN (short for Artificial Neural Network) model and a typical ARIMA (short for Autoregressive Integrated Moving Average) model are taken as the comparable models. The results show that, first, the LSTM model has strong generalization ability, with stable applicability in forecasting crude oil prices with different timescales. Second, as compared to other models, the LSTM model generally has higher forecasting accuracy for crude oil prices with different timescales. Third, an LSTM model-derived shorter forecast price timescale corresponds to a lower forecasting accuracy. Therefore, given a longer forecast crude oil price timescale, other factors may need to be included in the model.

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