DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

Liu, Chao; Ruan, Kaiyi; Ma, Xinmeng

doi:10.1016/j.heliyon.2023.e16715

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…Recent years have witnessed strong growth in adopting machine learning for time series forecasting, driven by its remarkable ability to uncover intri-cate and non-linear patterns within the data. Researchers have employed various Ml networks such as long-short-term memory (LSTM) [3,12,16], gated recurrent unite (GRU) [17,18,19], convolutional neural network (CNN) [20], and transformer models [21] to predict crude oil prices based on historical price data and some relevant features. Machine learning approaches often require extensive feature engineering and can be sensitive to the quality and availability of data.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks

Alruqimi,

Persio

2024

Preprint

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Despite numerous research efforts in applying deep learning to time series forecasting, achieving high accuracy in multi-step predictions for volatile time series like crude oil prices remains a significant challenge. Moreover, most existing approaches primarily focus on one-step forecasting, and the performance often varies depending on the dataset and specific case study. In this paper, we introduce an ensemble model to capture Brent oil price volatility and enhance the multi-step prediction. Our methodology employs a two-pronged approach. First, we assess popular deep-learning models and the impact of various external factors on forecasting accuracy. Then, we introduce an ensemble multi-step forecasting model for Brent oil prices. Our approach generates accurate forecasts by employing ensemble techniques across multiple forecasting scenarios using three BI-GRU networks. Extensive experiments were conducted on a dataset encompassing the COVID-19 pandemic period, which had a significant impact on energy markets. The proposed model's performance was evaluated using the standard evaluation metrics of MAE, MSE, and RMSE. The results demonstrate that the proposed model outperforms benchmark and established models.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks

Alruqimi,

Persio

2024

Preprint

View full text Add to dashboard Cite

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Enhancing Multi-step Brent Oil Price Forecasting with Ensemble Multi-scenario Bi-GRU Networks

Alruqimi,

Di Persio

2024

Int J Comput Intell Syst

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Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study

Xu,

Chan,

Haron

2024

PeerJ Computer Science

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As the pandemic continues to pose challenges to global public health, developing effective predictive models has become an urgent research topic. This study aims to explore the application of multi-objective optimization methods in selecting infectious disease prediction models and evaluate their impact on improving prediction accuracy, generalizability, and computational efficiency. In this study, the NSGA-II algorithm was used to compare models selected by multi-objective optimization with those selected by traditional single-objective optimization. The results indicate that decision tree (DT) and extreme gradient boosting regressor (XGBoost) models selected through multi-objective optimization methods outperform those selected by other methods in terms of accuracy, generalizability, and computational efficiency. Compared to the ridge regression model selected through single-objective optimization methods, the decision tree (DT) and XGBoost models demonstrate significantly lower root mean square error (RMSE) on real datasets. This finding highlights the potential advantages of multi-objective optimization in balancing multiple evaluation metrics. However, this study’s limitations suggest future research directions, including algorithm improvements, expanded evaluation metrics, and the use of more diverse datasets. The conclusions of this study emphasize the theoretical and practical significance of multi-objective optimization methods in public health decision support systems, indicating their wide-ranging potential applications in selecting predictive models.

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DMEformer: A newly designed dynamic model ensemble transformer for crude oil futures prediction

Cited by 3 publications

References 48 publications

Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks

Enhancing Multi-Step Brent Oil Price Forecasting with Ensemble Multi-Scenario Bi-GRU Networks

Enhancing Multi-step Brent Oil Price Forecasting with Ensemble Multi-scenario Bi-GRU Networks

Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study

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