Forecasting natural gas spot prices with nonlinear modeling using Gamma test analysis

Salehnia, Narges; Falahi, Mohammad Ali; Seifi, Ahmad; Adeli, Mohammad Hossein Mahdavi

doi:10.1016/j.jngse.2013.07.002

Cited by 53 publications

(22 citation statements)

References 34 publications

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“…The study reported by Salehnia (2013) is one of the few studies that really forecasts the spot price in the natural gas market [14]. Using Gamma test analysis, several nonlinear models including local linear regression (LLR), dynamic local linear regression (DLLR), and ANN models have been utilized to test spot prices (daily, weekly, and monthly) for Henry Hub from 1997-2012.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

Zhang

Zhu

et al. 2019

Energies

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Natural gas is often described as the cleanest fossil fuel. The consumption of natural gas is increasing rapidly. Accurate prediction of natural gas spot prices would significantly benefit energy management, economic development, and environmental conservation. In this study, the least squares regression boosting (LSBoost) algorithm was used for forecasting natural gas spot prices. LSBoost can fit regression ensembles well by minimizing the mean squared error. Henry Hub natural gas spot prices were investigated, and a wide range of time series from January 2001 to December 2017 was selected. The LSBoost method is adopted to analyze data series at daily, weekly and monthly. An empirical study verified that the proposed prediction model has a high degree of fitting. Compared with some existing approaches such as linear regression, linear support vector machine (SVM), quadratic SVM, and cubic SVM, the proposed LSBoost-based model showed better performance such as a higher R-square and lower mean absolute error, mean square error, and root-mean-square error.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

Zhang

Zhu

et al. 2019

Energies

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“…In addition, we checked the reference lists of the identified papers [60]. Overall, 15 articles have been identified [2], [7], [45], [26], [28], [33], [8], [32], [29], [39], [31], [46], [47], [61], [36]. For the majority of articles, the topic of price prognosis is in the research focus.…”

Section: Decision Support In Natural Gas Tradingmentioning

confidence: 99%

“…A comparison of all tickers in real time requests an aligned technology. Applied gas price prognosis models are usually performing time series analysis [47] and are working with structured data. A more sophisticated and integrated perspective on structured and unstructured data is already common in the finance industry.…”

Section: Introductionmentioning

confidence: 99%

Detecting similar news tickers in the area of natural gas trading

Dreikorn

Felden

Pospiech

et al. 2015

Proceedings of the 7th International Conference on Management of Computational and Collective intElligence in Digital EcoSystem

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The volatility of the natural gas market founded a need for the ability to analyze upcoming events in real time in order to manage profits and risks for participants. News ticker provide information being of utmost importance for the analysis. The research presented among this paper describes features of a software prototype supporting the analytical price prognosis tasks for gas traders. By knowing market development at the time of a certain past situation, the outcome of that situation can be used to predict the future market development of a current analyzed situation with similar content. For that, similar situations have to be detected in order to reduce uncertainty about future. Fitting into design science, we use task-technology-fit theory and technologyacceptance-model to identify information needs and to evaluate the artifact. This novel approach serves as a further step to gain a decision support with integrated structured and unstructured data.

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“…The second prediction category is the artificial intelligence method represented by artificial neural networks (ANN), support vector machines (SVM) and least square SVM (LSSVM). Salehnia et al (2013) use the ANN, local linear regression (LLR) and dynamic local linear regression (DLLR) models to forecast the spot prices of Henry Hub natural gas from January 1997 to March 2012. As demonstrated in their research results, ANN can achieve better prediction results than LLR and DLLR, among which DLLR is superior to LLR.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

Zhu

Shi

Chevallier

et al. 2016

Journal of Forecasting

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For forecasting nonstationary and nonlinear energy prices time series, a novel adaptive multiscale ensemble learning paradigm incorporating ensemble empirical mode decomposition (EEMD), particle swarm optimization (PSO) and least square support vector machines (LSSVM) with kernel function prototype is developed. Firstly, the extrema symmetry expansion EEMD, which can effectively restrain the mode mixing and end effects, is used to decompose the energy price into simple modes. Secondly, by using the fine‐to‐coarse reconstruction algorithm, the high‐frequency, low‐frequency and trend components are identified. Furthermore, autoregressive integrated moving average is applicable to predicting the high‐frequency components. LSSVM is suitable for forecasting the low‐frequency and trend components. At the same time, a universal kernel function prototype is introduced for making up the drawbacks of single kernel function, which can adaptively select the optimal kernel function type and model parameters according to the specific data using the PSO algorithm. Finally, the prediction results of all the components are aggregated into the forecasting values of energy price time series. The empirical results show that, compared with the popular prediction methods, the proposed method can significantly improve the prediction accuracy of energy prices, with high accuracy both in the level and directional predictions. Copyright © 2016 John Wiley & Sons, Ltd.

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Forecasting natural gas spot prices with nonlinear modeling using Gamma test analysis

Cited by 53 publications

References 34 publications

Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm

Detecting similar news tickers in the area of natural gas trading

An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting

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