Thermal coal price forecasting via the neural network
Thermal coal price forecasts represent an essential issue to investors and policy makers, given its importance as a strategic energy source. The current work aims at exploring usefulness of non-linear auto-regressive neural networks for this forecast problem based upon a data-set of closing prices r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Intelligent Systems with Applications |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305322000242 |
| _version_ | 1811195632860266496 |
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| author | Xiaojie Xu Yun Zhang |
| author_facet | Xiaojie Xu Yun Zhang |
| author_sort | Xiaojie Xu |
| collection | DOAJ |
| description | Thermal coal price forecasts represent an essential issue to investors and policy makers, given its importance as a strategic energy source. The current work aims at exploring usefulness of non-linear auto-regressive neural networks for this forecast problem based upon a data-set of closing prices recorded on a daily basis of thermal coal traded in China Zhengzhou Commodity Exchange during January 4, 2016 – December 31, 2020, which is an important financial index not sufficiently explored in the literature in terms of its price forecasts. Through testing a variety of model settings over algorithms, delays, hidden neurons, and data splitting ratios, the model that produces performance of good accuracy and stabilities is reached. Particularly, the model has five delays and ten hidden neurons and is constructed with the Levenberg-Marquardt algorithm based on the ratio of 80%–10%–10% of the data for training–validation–testing. It leads to relative root mean square errors of 1.48%, 1.49%, and 1.47% for the training, validation, and testing phases, respectively. Usefulness of neural networks for the price forecast issue of thermal coal is demonstrated. Forecast results here could serve as standalone technical forecasts and be combined with other forecasts when conducting policy analysis that involves forming perspectives of trends in prices. |
| first_indexed | 2024-04-12T00:46:27Z |
| format | Article |
| id | doaj.art-b59e70fbf4304ffc90eb3c3d95d3802c |
| institution | Directory Open Access Journal |
| issn | 2667-3053 |
| language | English |
| last_indexed | 2024-04-12T00:46:27Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj.art-b59e70fbf4304ffc90eb3c3d95d3802c2022-12-22T03:54:52ZengElsevierIntelligent Systems with Applications2667-30532022-05-0114200084Thermal coal price forecasting via the neural networkXiaojie Xu0Yun Zhang1Corresponding author.; North Carolina State University, Raleigh, NC 27695, USNorth Carolina State University, Raleigh, NC 27695, USThermal coal price forecasts represent an essential issue to investors and policy makers, given its importance as a strategic energy source. The current work aims at exploring usefulness of non-linear auto-regressive neural networks for this forecast problem based upon a data-set of closing prices recorded on a daily basis of thermal coal traded in China Zhengzhou Commodity Exchange during January 4, 2016 – December 31, 2020, which is an important financial index not sufficiently explored in the literature in terms of its price forecasts. Through testing a variety of model settings over algorithms, delays, hidden neurons, and data splitting ratios, the model that produces performance of good accuracy and stabilities is reached. Particularly, the model has five delays and ten hidden neurons and is constructed with the Levenberg-Marquardt algorithm based on the ratio of 80%–10%–10% of the data for training–validation–testing. It leads to relative root mean square errors of 1.48%, 1.49%, and 1.47% for the training, validation, and testing phases, respectively. Usefulness of neural networks for the price forecast issue of thermal coal is demonstrated. Forecast results here could serve as standalone technical forecasts and be combined with other forecasts when conducting policy analysis that involves forming perspectives of trends in prices.http://www.sciencedirect.com/science/article/pii/S2667305322000242Thermal coalPrice forecastsTime series dataNeural networksMachine learning technique |
| spellingShingle | Xiaojie Xu Yun Zhang Thermal coal price forecasting via the neural network Intelligent Systems with Applications Thermal coal Price forecasts Time series data Neural networks Machine learning technique |
| title | Thermal coal price forecasting via the neural network |
| title_full | Thermal coal price forecasting via the neural network |
| title_fullStr | Thermal coal price forecasting via the neural network |
| title_full_unstemmed | Thermal coal price forecasting via the neural network |
| title_short | Thermal coal price forecasting via the neural network |
| title_sort | thermal coal price forecasting via the neural network |
| topic | Thermal coal Price forecasts Time series data Neural networks Machine learning technique |
| url | http://www.sciencedirect.com/science/article/pii/S2667305322000242 |
| work_keys_str_mv | AT xiaojiexu thermalcoalpriceforecastingviatheneuralnetwork AT yunzhang thermalcoalpriceforecastingviatheneuralnetwork |