A compressed sensing based AI learning paradigm for crude oil price forecasting

Yu, Lean; Zhao, Yiwen; Tang, Ling

doi:10.1016/j.eneco.2014.09.019

Cited by 115 publications

(41 citation statements)

References 46 publications

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“…First, different from other traditional models, a much more powerful method with its merits of handling the problem of data complexity, i.e., WD-FNN-ADD, is employed in hospital visits forecasting. Second, to the best of our knowledge, although the principle of "decomposition and ensemble" has be widely applied in time series forecasting, e.g., crude oil price (Garg et al, 2012;Tang et al, 2012;Yu et al, 2014;Yu et al, 2015), hydropower consumption and nuclear consumption forecasting , such a hybrid decomposition-and-ensemble model, i.e., WD-FNN-ADD, hasn't been introduced to hospital visits analysis or forecasting research. Therefore, the gap is filled via this study applying WD-FNN-ADD to the field of hospital visits forecasting.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Hang

Tang

et al. 2017

EURASIA J MATH SCI T

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Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of "decomposition and ensemble". In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.

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Section: Discussionmentioning

confidence: 99%

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Hang

Tang

et al. 2017

EURASIA J MATH SCI T

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“…Tang et al [22] formulated a novel decompositionensemble learning paradigm for crude oil price forecasting by utilizing the data decomposition tool of complementary ensemble EMD (CEEMD), and the results supported the efficiency of the decomposition strategy in improving model performance. Yu et al [1] proposed a compressed sensing based AI learning paradigm for daily crude oil price forecasting and achieved a similar conclusion, with the sample period from January 3, 2011 to July 17, 2013. Yu et al [23] proposed a novel learning paradigm based on ensemble EMD (EEMD) and extended extreme learning machine (EELM), to predict the WTI daily crude oil prices from January 2, 1986 to October 21, 2013.…”

Section: Introductionmentioning

confidence: 58%

“…However, crude oil price forecasting has fully been proven to be an extremely difficult task [1]. On the one hand, like other commodities, crude oil price is driven by various market factors, e.g., supply and demand.…”

Section: Introductionmentioning

confidence: 99%

A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

2015

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“…Moreover, besides the traditional GA, other more advanced GA techniques, e.g., the covariance matrix adaption evolutionary strategy [79], can be introduced to improve the proposed model especially in terms of time efficiency. Finally, the promising concept of ''decomposition and ensemble'' [80][81][82] can be also introduced to enhance the analysis and prediction capability of the novel model. We will look into these interesting issues in the near future.…”

Section: Discussionmentioning

confidence: 99%

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

Dai

Tang

et al. 2015

Neural Comput & Applic

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In order to effectively model crude oil spot price with inherently high complexity, a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR [consisting of grid method and genetic algorithm (GA)], i.e., a hybrid grid-GA-based LSSVR model, is proposed in this study. In the proposed hybrid learning paradigm, the grid method, a simple but efficient searching method, is first applied to roughly but rapidly determine the proper boundaries of the parameters in the LSSVR; then, the GA, an effective and powerful intelligent searching algorithm, is further implemented to select the most suitable parameters. For illustration and verification, the proposed learning paradigm is used to predict the crude oil spot prices of the West Texas Intermediate and the Brent markets. The empirical results demonstrate that the proposed hybrid grid-GA-based LSSVR learning paradigm can outperform its benchmarking models (including some popular forecasting techniques and similar LSSVRs with other parameter searching algorithms) in terms of both prediction accuracy and time-savings, indicating that it can be utilized as one effective forecasting tool for crude oil price with high volatility and irregularity.

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A compressed sensing based AI learning paradigm for crude oil price forecasting

Cited by 115 publications

References 46 publications

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

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A compressed sensing based AI learning paradigm for crude oil price forecasting

Cited by 115 publications

References 46 publications

Forecasting Patient Visits to Hospitals using a WD&amp;ANN-based Decomposition and Ensemble Model

Forecasting Patient Visits to Hospitals using a WD&amp;ANN-based Decomposition and Ensemble Model

A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

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Product

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Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model