Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model

Qin, Quande; Huang, Zhaorong; Zhou, Zhihao; Chen, Chen; Liu, Rui

doi:10.1016/j.engappai.2023.106266

Cited by 20 publications

(3 citation statements)

References 60 publications

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“…Qin et al compared popular single-model and multiple-model ML methods used for crude oil price prediction by applying online data from Google Trends to enhance the prediction ability. The experimental results indicated that introducing Google Trends can improve prediction performance, and the multiple-model approach indicated higher prediction accuracy [27].…”

Section: Energy Prices Prediction Using ML Algorithmsmentioning

confidence: 97%

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Yang

Choi

2023

Energies

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The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG.

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Section: Energy Prices Prediction Using ML Algorithmsmentioning

confidence: 97%

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Yang

Choi

2023

Energies

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“…Wu et al (2019) integrated Complete Ensemble Empirical Mode Decomposition (CEEMD), ARIMA, and Sparse Bayesian Learning (SBL) to create a robust predictive framework. Gasper & Mbwambo (2023) sought insights by applying ARIMA models, while Qin et al (2023) harnessed the power of Machine Learning in tandem with Google Search data to fine-tune their predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling And Enhancing Crude Oil Price Forecasting Using Enhanced Set Algebra-Based Heuristic Algorithm-Based Extreme Learning Machine

Behera,

Kumar,

Nayak

2024

Preprint

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This study has two main aspects. Firstly, we combined the Nelder-Mead Simplex Algorithm with the Set Algebra-Based Heuristic Algorithm (SAHA) in order to improve SAHA's capacity to do local searches. This integration resulted in a hybrid learning approach known as ESAHA. After that, we use the Enhanced Simulated Annealing with Hybrid Algorithm (ESAHA) to improve six benchmark functions so that we can see how well ESAHA works. Furthermore, we utilize ESAHA hybrid learning to enhance the weights and biases of an Extreme Learning Machine (ELM), resulting in the creation of a hybrid model referred to as ESAHA-ELM. We utilize the ESAHA-ELM model to predict the final price of crude oil datasets. In addition, we employ the SAHA, BMO, PSO, and GA algorithms to train the ELM and generate four alternative models for the purpose of comparison in the forecasting job. In order to examine the predictive accuracy of each model, we utilize the MAPE and MSE error metrics. Additionally, we implement the Prediction of Change in Direction (POCID) statistical test to determine if there are any significant differences between the models. The experimental investigation shows that the ESAHA-ELM model has statistical relevance in accurately capturing the inherent volatility of financial time series. In addition, it surpasses other models such as SAHA-ELM, MBO-ELM, PSO-ELM, and GA-ELM.

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“…The mathematical methods mainly consist of three elements: statistical machine learning, model, strategy, and algorithm. A time series is a classic trend prediction method that assumes future values composed of patterns of current and historical data, and it is applied to constructing a model from historical data and then predict future data in machine learning (Caicedo-Torres & Payares, 2016;Mehmood et al, 2022;Qin et al, 2023;Sun & Lu, 2023). According to Calero-Sanz et al (2022), Jiang et al (2023), and Yang et al (2015), the tourism sector, especially the hotel industry, has adopted machine learning for forecasting room booking and cancellation, demand, prices, and occupancy.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Algorithms to Predict Hotel Occupancy

Kozlovskis,

Liu,

Lace

et al. 2023

Journal of Business Economics and Management

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The development and availability of information technology and the possibility of deep integration of internal IT systems with external ones gives a powerful opportunity to analyze data online based on external data providers. Recently, machine learning algorithms play a significant role in predicting different processes. This research aims to apply several machine learning algorithms to predict high frequent daily hotel occupancy at a Chinese hotel. Five machine learning models (bagged CART, bagged MARS, XGBoost, random forest, SVM) were optimized and applied for predicting occupancy. All models are compared using different model accuracy measures and with an ARDL model chosen as a benchmark for comparison. It was found that the bagged CART model showed the most relevant results (R2 > 0.50) in all periods, but the model could not beat the traditional ARDL model. Thus, despite the original use of machine learning algorithms in solving regression tasks, the models used in this research could have been more effective than the benchmark model. In addition, the variables’ importance was used to check the hypothesis that the Baidu search index and its components can be used in machine learning models to predict hotel occupancy.

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Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model

Cited by 20 publications

References 60 publications

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

Modeling And Enhancing Crude Oil Price Forecasting Using Enhanced Set Algebra-Based Heuristic Algorithm-Based Extreme Learning Machine

Application of Machine Learning Algorithms to Predict Hotel Occupancy

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