Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models

mu, Hacer Yumurtaci Aydo; Ekinci, Aykut; Erdal, Halil İbrahim; Erdal, Hami̇t

doi:10.5897/jeif2014.0629

Cited by 22 publications

(3 citation statements)

References 35 publications

(39 reference statements)

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“…Hacer et al [15] tried to forecast the monthly crude oil price using the bagging ensemble models and found that classification and regression trees perform better than artificial neural network-based models. With the idea of bagging being introduced, they claimed to have gained better accuracy in both the models.…”

Section: Literature Reviewmentioning

confidence: 99%

An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach

2023

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Crude Oil is one of the most important commodities in this world. We have studied the effects of Crude Oil inventories on crude oil prices over the last ten years (2011 to 2020). We tried to figure out how the Crude Oil price variance responds to inventory announcements. We then introduced several other financial instruments to study the relation of these instruments with Crude Oil variation. To undertake this task, we took the help of several mathematical tools including machine learning tools such as Long Short Term Memory(LSTM) methods, etc. The previous researches in this area primarily focussed on statistical methods such as GARCH (1,1) etc. (Bu (2014)). Various researches on the price of crude oil have been undertaken with the help of LSTM. But the variation of crude oil price has not yet been studied. In this research, we studied the variance of crude oil prices with the help of LSTM. This research will be beneficial for the options traders who would like to get benefit from the variance of the underlying instrument.

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Section: Literature Reviewmentioning

confidence: 99%

An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach

2023

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“…While in 2015, Aydogmus and Ekinci mainly investigated bagging ensemble models such as bagged artificial neural networks (BANN) and bagged regression trees (BRT). It is concluded that the bagging ensemble method could optimize the forecast accuracy [10]. Those papers have suggested ensemble learning algorithms have the potential to work well enough in forecasting areas.…”

Section: Introductionmentioning

confidence: 99%

Crude Oil Price Prediction Based on Multiple Ensemble Learning Algorithms

Zhang¹

2023

BCPBM

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The volatility of crude oil price affects people from all walks of life. Since the start of the 21st century, the frequent booms and busts in crude oil price have caused a greater emphasis on forecasting the price. In order to make prediction from a new perspective, this paper combined both financial and non-financial factors and applied both bagging and boosting algorithms in ensemble learning to construct models. According to the results, both bagging and boosting algorithms achieved impressive improvements over the benchmark model. The ensemble learning models can not only reach high goodness-of-fit, but also attain excellent accuracy in direction prediction, which indicates the models can well realize the long term trend prediction of crude oil price. In brief, this paper enriched the content in predicting crude oil price and offer a guideline for the application of ensemble learning algorithms in such a field.

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“…In the fourth category, because the ensemble model is the integrated prediction of multiple same-type models, it can solve the issues that caused by the single model. It mainly include Bagging [34,35], Boosting [36], AdaBoost [37], and XGBoost [38,39], etc. At present, the ensemble model is widely used in various domains.…”

Section: Introductionmentioning

confidence: 99%

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

2021

Energies

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In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction.

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Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models

Cited by 22 publications

References 35 publications

An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach

An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach

Crude Oil Price Prediction Based on Multiple Ensemble Learning Algorithms

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

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