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

Su, Moting; Zhang, Zongyi; Zhu, Ye; Zha, Donglan

doi:10.3390/en12061094

Cited by 51 publications

(21 citation statements)

References 41 publications

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“…Despite this fact, no detailed analysis of the forecasting performances these similar models have been applied in the natural gas markets. Most of studies being conducted for natural gas price forecasting rely heavily on the artificial neural network approaches (MacAvoy and Moshkin, 2000;Buchananan et al, 2001;Nguyen and Nabney, 2010;Abrishami and Varahrami, 2011;Azadeh et al, 2012;Busse et al, 2012;Salehnia et al, 2013;Mishra and Smyth, 2016;Čeperić et al, 2017;Su et al, 2019). Therefore, the existence of structural instability, which is documented in more recent studies, is likely to be overlooked in these studies.…”

Section: Introductionmentioning

confidence: 99%

Forecasting natural gas prices using highly flexible time-varying parameter models

2021

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The growing disintegration between the natural gas and oil prices, together with shale revolution and market financialization, lead to continued fundamental changes in the natural gas markets. To capture these structural changes, this paper considers a wide set of highly flexible time-varying parameter models to evaluate the out-of-sample forecasting performance of the natural gas spot prices across the US, European and Japanese markets. The results show that for both Japan and EU markets, the best forecasting performance is found when the model allows for drastic changes in the conditional mean and gradual changes in the conditional volatility. For the US market, however, no model performs systematically better than the simple autoregressive model. Full sample estimation results further confirm that allowing t-distributed error is important in modelling the natural gas prices, especially for EU markets.

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Section: Introductionmentioning

confidence: 99%

Forecasting natural gas prices using highly flexible time-varying parameter models

2021

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“…The empirical results showed that it effectively improved the short-term price forecasting accuracy compared with other listed models. Su et al [44] proposed the least squares regression boosting (LSBoost) algorithm to forecast the natural gas spot prices. The empirical results revealed that this method had a superior prediction performance.…”

Section: Introductionmentioning

confidence: 99%

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

2021

Energies

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In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction.

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“…One such technique, which has been gaining high popularity in recent years, is support vector regression (SVR). In particular, SVR and SVR-based hybrid models have been applied in many studies to forecast prices of energy commodities, including: crude oil (e.g., [13][14][15][16][17][18]) and natural gas (e.g., [19,20]). However, they have been used only by Zhang and Zhang [8] to forecast volatility of energy commodities, specifically crude oil.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

2020

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We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

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

Cited by 51 publications

References 41 publications

Forecasting natural gas prices using highly flexible time-varying parameter models

Forecasting natural gas prices using highly flexible time-varying parameter models

A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

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