A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Zhou, Yingrui; Li, Taiyong; Shi, Junqin; Qian, Zijie

doi:10.1155/2019/4392785

Cited by 94 publications

(56 citation statements)

References 46 publications

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“…To cope with that, a "decomposition and ensemble" framework was widely introduced into time series forecasting. The first step of the "decomposition and ensemble" framework is to decompose the complex raw time series into a group of relatively simple components, then a single predictor is introduced to predict each component independently, and finally these predicted values of all components are assembled as the final predicted results [24][25][26][27]. This idea has also been introduced into the field of crude oil price prediction.…”

Section: Introductionmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.

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Section: Introductionmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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“…XGBoost can automatically use a CPU's multithreaded calculations, thereby reducing runtime and improving algorithm accuracy. Zhou et al [Zhou, Li, Shi et al 2019] provided a detailed derivation and calculation method of the XGBoost algorithm.…”

Section: Xgboostmentioning

confidence: 99%

Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

Wang¹,

Yan²

2019

Computer Modeling in Engineering &Amp; Sciences

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A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between variables. By contrast, ensemble models can effectively solve this problem. Three key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel, the diversity between subsample sets and the optimal ensemble method. This study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the model. Our proposed method first uses a bagging algorithm to generate multiple subsample sets. Second, an indicator vector is defined to describe these subsample sets. Third, subsample sets are selected on the basis of the results of agglomerative nesting clustering on indicator vectors to maximize the diversity between subsets. Subsequently, these subsample sets are placed in a stacked autoencoder for training. Finally, XGBoost algorithm, rather than the traditional simple average ensemble method, is imported to ensemble the model during modeling. Three machine learning public datasets and atmospheric column dry point dataset from a practical industrial process show that our proposed method demonstrates high precision and improved prediction ability.

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“…e main idea of this framework is to decompose the raw energy data series into several simpler components, then handle each component individually, and finally integrate the result from each component as the final forecasting result. is framework is a typical form of the strategy of "divide and conquer" that is widely used in energy price forecasting [22][23][24], wind speed forecasting [25,26], load forecasting [27,28], biosignal processing [29,30], fault diagnosis [31], image processing [32][33][34][35], and so on. ere are many types of decomposition methods that can be applied to decomposing energy time series.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

Qian

He³

2020

Complexity

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Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.

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A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Cited by 94 publications

References 46 publications

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM

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