A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting

Ding, Yishan

doi:10.1016/j.energy.2018.04.133

Cited by 85 publications

(25 citation statements)

References 48 publications

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“…Lahmiri (2015) reaches similar results when applying VMD-based generalized regression neural network (GRNN) ensemble model over Empirical Mode Decomposition (EMD) models to estimate California electricity and Brent crude oil price. Ding (2018) proposes a new hybrid model using Akaike Information Criteria (AIC), EEMD and ANN models. He suggested selection of lag using AIC, decomposing the variable using EEMD and forecast through ANN and ADD (Additional Ensemble Method).…”

Section: Machine Learning Modelsmentioning

confidence: 99%

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A novel hybrid approach to forecast crude oil futures using intraday data

Manickavasagam

Visalakshmi

Apergis

2020

Technological Forecasting and Social Change

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Prediction of oil prices is an implausible task due to the multifaceted nature of oil markets. This study presents two novel hybrid models to forecast WTI and Brent crude oil prices using combinations of machine learning and nature inspired algorithms. The first approach, MARSplines-IPSO-BPNN, Multivariate Adaptive Regression Splines (MARSPlines) find the important variables that affect crude oil prices. Then, the selected variables are fed into an Improved Particle Swarm Optimization (IPSO) method to obtain the best estimates of the parameters of the Backpropagation Neural Network (BPNN). Once these parameters are obtained, the variables are fed into the BPNN model to generate the required forecasts. The second approach, MARSplines-FPA-BPNN, generates the parameters of BPNN through the Flower Pollination Algorithm (FPA). The forecasting ability of these new models is compared to certain benchmark models. The findings document that the MARSplines-FPA-BPNN model performs better than the other competitive models.

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Section: Machine Learning Modelsmentioning

confidence: 99%

“…Currently, researchers are building hybrid models by combining two or more models and finding it as a successful process in minimizing the deviation between actual and predicted prices (Azadeh et al, 2015;Chai et al, 2018b;Ding, 2018;Chen et al, 2019). This motivates this paper to build novel hybrid models.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid approach to forecast crude oil futures using intraday data

Manickavasagam

Visalakshmi

Apergis

2020

Technological Forecasting and Social Change

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“…The feedforward (FF) neural network used in this paper is based on a multi-layer feedforward network, which is composed of three layers [22,23]. It can efficiently train the network using a gradient-based optimization algorithm.…”

Section: The Fundamentals Of the Ff Modelmentioning

confidence: 99%

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

Ren

Lin

2019

Energies

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It is well known that the inherent instability of wind speed may jeopardize the safety and operation of wind power generation, consequently affecting the power dispatch efficiency in power systems. Therefore, accurate short-term wind speed prediction can provide valuable information to solve the wind power grid connection problem. For this reason, the optimization of feedforward (FF) neural networks using an improved flower pollination algorithm is proposed. First of all, the empirical mode decomposition method is devoted to decompose the wind speed sequence into components of different frequencies for decreasing the volatility of the wind speed sequence. Secondly, a back propagation neural network is integrated with the improved flower pollination algorithm to predict the changing trend of each decomposed component. Finally, the predicted values of each component can get into an overlay combination process and achieve the purpose of accurate prediction of wind speed. Compared with major existing neural network models, the performance tests confirm that the average absolute error using the proposed algorithm can be reduced up to 3.67%.

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“…The authors of [53] combined results of bivariate empirical mode decomposition, interval Multilayer Perceptrons, and an interval exponential smoothing method to predict crude oil prices. Other interesting approaches using ensembles are the use of multiple feed forward neural networks [54], multiple artificial neural networks with model selection [55], among others.…”

Section: Background and Related Workmentioning

confidence: 99%

A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

et al. 2019

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Financial markets forecasting represents a challenging task for a series of reasons, such as the irregularity, high fluctuation, noise of the involved data, and the peculiar high unpredictability of the financial domain. Moreover, literature does not offer a proper methodology to systematically identify intrinsic and hyper-parameters, input features, and base algorithms of a forecasting strategy in order to automatically adapt itself to the chosen market. To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy, created by a set of classifiers with intrinsic and hyper-parameters learned in each marked under consideration. A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical state-of-the-art solutions.

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A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting

Cited by 85 publications

References 48 publications

A novel hybrid approach to forecast crude oil futures using intraday data

A novel hybrid approach to forecast crude oil futures using intraday data

Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction

A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

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