A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events

Cheng, Yuxiang; Yi, Jiayu; Yang, Xibei; Lai, Kin Keung; Seco, Luis

doi:10.1007/s00500-022-07276-5

Cited by 10 publications

(6 citation statements)

References 65 publications

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“…In reference to the study of oil prices, various methods have been used to make predictions and analyze the factors affecting its fluctuation. The research in [26] utilized the Complementary Empirical Ensemble Mode Decomposition (CEEMD) to break down the barrel price into its components and identify the impact of extreme events on crude oil prices. The researcher combined the Iterative Cumulative Sum of Squares (ICSS) test and Chow's test to detect structural breaks, then used ARIMA and SVM models to forecast oil prices.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Oil Price Forecasting Through an Intelligent Hybridized Approach

BOUSSATTA,

CHIHAB,

CHIHAB

et al. 2023

IJACSA

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The oil market has long experienced price fluctuations driven by diverse factors. These shifts in crude oil prices wield substantial influence over the costs of various goods and services. Moreover, the price per barrel is intricately intertwined with global economic activities, themselves influenced by the trajectory of oil prices. Analyzing oil behavior stands as a pivotal means for tracking the evolution of barrel prices and predicting future oil costs. This analytical approach significantly contributes to the field of crude oil price forecasting. Researchers and scientists alike prioritize accurate crude oil price forecasting. Yet, such endeavors are often challenged by the intricate nature of oil price behavior. Recent times have witnessed the effective employment of various approaches, including Hybrid and Machine Learning techniques to address similarly complex tasks, though they often yield elevated error rates, as observed in financial markets. In this study, the goal is to enhance the predictive precision of several weak supervised learning predictors by harnessing hybridization, particularly within the context of the crude oil market's multifaceted variations. The focus extends to a vast dataset encompassing CPSE Stock ETF prices over a period of 23 years. Ten distinct models, namely SVM, XGBoost, Random Forest, KNN, Gradient Boosting, Decision Tree, Ridge, Lasso, Elastic Net, and Neural Network, were employed to derive elemental predictions. These predictions were subsequently amalgamated via Linear Regression, yielding heightened performance. The investigation underscores the efficacy of hybridization as a strategy. Ultimately, the proposed approach's performance is juxtaposed against its individual weak predictors, with experiment results validating the findings.

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Section: Related Workmentioning

confidence: 99%

Enhancing Oil Price Forecasting Through an Intelligent Hybridized Approach

BOUSSATTA,

CHIHAB,

CHIHAB

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…The results all showed that the hybrid models have stronger robustness than the individual models. Cheng [17] used CEEMD to decompose crude oil price data and obtained structural breaks using ICSS and the Chow test, and combined the decomposed data and structural breaks to build a CEEMD-ARIMA-SVM model, which experimentally proved that the hybrid model has better results in dealing with complex data and can be used in many fields.…”

Section: Decomposition Of Time Series Datamentioning

confidence: 99%

Improved BIGRU Model and Its Application in Stock Price Forecasting

et al. 2023

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In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The R2, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm’s goodness-of-fit value will greatly improve the accuracy of stock price predictions.

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“…The first risk is commodities price risk, this may come from the Ukraine war, and as exports of wheat, corn, and other raw materials that ABF utilizes to produce its products are decreasing, the prices of such commodities may fluctuate considerably in months [6][7][8]. Since the prices of the raw materials, such as wheat and corn, have already risen sharply, whether their prices will return to a pre-war situation or keep soaring is still unknown [9].…”

Section: Hedging Strategies Analysis 31 Potential Risks Of the Companymentioning

confidence: 99%

Financial Analysis and Hedging Strategy Analysis of Associated British Foods

Zhang¹

2022

BCPBM

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A multinational food company may have to face various risks from its supply chain or other aspects. Hedging strategies by using financial derivatives such as futures contracts are useful for such companies to reduce their risks. This paper aims to make proposals for Associated British Foods (ABF) to reduce its risks by using the future contracts and options of its raw materials. Based on the analysis of the background and financial situation of ABF, it is suggested that future contracts of wheat and other raw materials and their options can be hedged. Long call, short call, bull spread, and bear spread are analyzed respectively based on the latest market data. The paper only takes one example of CHICAGO SRW WHEAT (ZWU2), but it is suggested that a number of related commodities can be hedged in similar methods.

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A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events

Cited by 10 publications

References 65 publications

Enhancing Oil Price Forecasting Through an Intelligent Hybridized Approach

Enhancing Oil Price Forecasting Through an Intelligent Hybridized Approach

Improved BIGRU Model and Its Application in Stock Price Forecasting

Financial Analysis and Hedging Strategy Analysis of Associated British Foods

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