Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

Commodity markets, such as crude oil and precious metals, play a strategic role in the economic development of nations, with crude oil prices influencing geopolitical relations and the global economy. Moreover, gold and silver are argued to hedge the stock and cryptocurrency markets during market downsides. Therefore, accurate forecasting of crude oil and precious metals prices is critical. Nevertheless, due to the nonlinear nature, substantial fluctuations, and irregular cycles of crude oil and precious metals, predicting their prices is a challenging task. Our study contributes to the commodity market price forecasting literature by implementing and comparing advanced deep-learning models. We address this gap by including silver alongside gold in our analysis, offering a more comprehensive understanding of the precious metal markets. This research expands existing knowledge and provides valuable insights into predicting commodity prices. In this study, we implemented 16 deep- and machine-learning models to forecast the daily price of the West Texas Intermediate (WTI), Brent, gold, and silver markets. The employed deep-learning models are long short-term memory (LSTM), BiLSTM, gated recurrent unit (GRU), bidirectional gated recurrent units (BiGRU), T2V-BiLSTM, T2V-BiGRU, convolutional neural networks (CNN), CNN-BiLSTM, CNN-BiGRU, temporal convolutional network (TCN), TCN-BiLSTM, and TCN-BiGRU. We compared the forecasting performance of deep-learning models with the baseline random forest, LightGBM, support vector regression, and k-nearest neighborhood models using mean absolute error (MAE), mean absolute percentage error, and root mean squared error as evaluation criteria. By considering different sliding window lengths, we examine the forecasting performance of our models. Our results reveal that the TCN model outperforms the others for WTI, Brent, and silver, achieving the lowest MAE values of 1.444, 1.295, and 0.346, respectively. The BiGRU model performs best for gold, with an MAE of 15.188 using a 30-day input sequence. Furthermore, LightGBM exhibits comparable performance to TCN and is the best-performing machine-learning model overall. These findings are critical for investors, policymakers, mining companies, and governmental agencies to effectively anticipate market trends, mitigate risk, manage uncertainty, and make timely decisions and strategies regarding crude oil, gold, and silver markets.

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Analyzing Internal and External Factors in Livestock Supply Forecasting Using Machine Learning: Sustainable Insights from South Korea

Chuluunsaikhan,

Kim,

Park

et al. 2024

Sustainability

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The supply of livestock products depends on many internal and external factors. Omitting any one factor can make it difficult to describe the market patterns. So, forecasting livestock indexes such as prices and supplies is challenging due to the effect of unknown factors. This paper proposes a Stacking Forest Ensemble method (SFE-NET) to forecast pork supply by considering both internal and external factors, thereby contributing to sustainable pork production. We first analyze the internal factors to explore features related to pork supply. External factors such as weather conditions, gas prices, and disease information are also collected from different sources. The combined dataset is from 2016 to 2022. Our SFE-NET method utilizes Random Forest, Gradient Boosting, and XGBoost as members and a neural network as the meta-method. We conducted seven experiments for daily, weekly, and monthly pork supply using different sets of factors, such as internal, internal and external, and selected. The results showed the following findings: (a) The proposed method achieved Coefficient of Determination scores between 84% and 91% in short and long periods, (b) the external factors increased the performance of forecasting methods by about 2% to 12%, and (c) the proposed stacking ensemble method outperformed other comparative methods by 1% to 18%. These improvements in forecasting accuracy can help promote more sustainable pork production by enhancing market stability and resilience.

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Forecasting the Crude Oil Spot Price with Bayesian Symbolic Regression

Cited by 3 publications

References 136 publications

Forecasting Commodities Prices with the Bayesian Symbolic Regression Compared to Other Methods

Forecasting Commodities Prices with the Bayesian Symbolic Regression Compared to Other Methods

Deep learning systems for forecasting the prices of crude oil and precious metals

Analyzing Internal and External Factors in Livestock Supply Forecasting Using Machine Learning: Sustainable Insights from South Korea

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