Measurement: Energy最新文献

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Price forecasting through neural networks for crude oil, heating oil, and natural gas 通过神经网络预测原油、取暖油和天然气的价格

Measurement: Energy

Pub Date : 2024-03-01 DOI: 10.1016/j.meaene.2024.100001

Bingzi Jin , Xiaojie Xu

Building price projections of various energy commodities has long been an important endeavor for a wide range of participants in the energy market. We study the forecast problem in this paper by concentrating on four significant energy commodities. Using nonlinear autoregressive neural network models, we investigate the daily prices of WTI and Brent crude oil as well as the monthly prices of Henry Hub natural gas and New York Harbor No. 2 heating oil. We investigate prediction performance resulting from various model configurations, including training techniques, hidden neurons, delays, and data segmentation. Based on the investigation, relatively straightforward models are built that yield quite accurate and reliable performance. Specifically, performance in terms of relative root mean square errors is 1.96%/1.81%/9.75%/21.76%, 1.96%/1.80%/8.76%/14.41%, and 1.87%/1.78%/9.10%/16.97% for model training, validation, and testing, respectively, and the overall relative root mean square error is 1.95%/1.80%/9.51%/20.35% for the whole sample for WTI crude oil/Brent crude oil/New York Harbor No. 2 heating oil/Henry Hub natural gas. The outcomes of this projection might be used in technical analysis or integrated with other fundamental forecasts for policy analysis.

长期以来，对各种能源商品进行价格预测一直是能源市场众多参与者的一项重要工作。我们在本文中集中研究了四种重要能源商品的预测问题。利用非线性自回归神经网络模型，我们研究了 WTI 和布伦特原油的每日价格，以及 Henry Hub 天然气和纽约港 2 号取暖油的每月价格。我们研究了各种模型配置的预测性能，包括训练技术、隐藏神经元、延迟和数据分割。在调查的基础上，我们建立了相对简单的模型，其性能相当准确可靠。具体来说，在模型训练、验证和测试中，相对均方根误差的性能分别为 1.96%/1.81%/9.75%/21.76%、1.96%/1.80%/8.76%/14.41% 和 1.87%/1.78%/9.10%/16.97%；在整个样本中，WTI 原油/布伦特原油/纽约港 2 号取暖油/亨利枢纽天然气的总体相对均方根误差为 1.95%/1.80%/9.51%/20.35%。这一预测结果可用于技术分析，或与其他基本面预测相结合用于政策分析。

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