数据采掘技术在石油价格预测中的应用
Petroleum prices prediction using data mining techniques -- A Review
摘要：
在过去20年中，肯尼亚对石油产品的需求激增。这主要是因为这种特殊商品用于该国经济的许多部门。汇率受到不断变化的价格的影响，这也影响到肯尼亚的工业商品产量。石油产品价格的任何变化都会对生产的其他物品的成本甚至经济的扩张产生重大影响。因此，准确的石油价格预测对于制定适合遏制燃料相关冲击的政策至关重要。数据采掘技术是用来发现数据中有价值模式的工具。石油价格预测中使用的数据采掘技术，包括人工神经网络（ANN）、支持向量机（SVM）和智能优化技术，如遗传算法（GA），已经变得越来越流行。这项研究提供了一个全面的审查现有的数据采掘技术，使石油价格预测。数据采掘技术分为回归模型、深度神经网络模型、模糊集和逻辑以及混合模型。这些模型是如何开发和模型的准确性提供了详细的讨论。
英文摘要：
Over the past 20 years, Kenya's demand for petroleum products has proliferated. This is mainly because this particular commodity is used in many sectors of the country's economy. Exchange rates are impacted by constantly shifting prices, which also impact Kenya's industrial output of commodities. The cost of other items produced and even the expansion of the economy is significantly impacted by any change in the price of petroleum products. Therefore, accurate petroleum price forecasting is critical for devising policies that are suitable to curb fuel-related shocks. Data mining techniques are the tools used to find valuable patterns in data. Data mining techniques used in petroleum price prediction, including artificial neural networks (ANNs), support vector machines (SVMs), and intelligent optimization techniques like the genetic algorithm (GA), have grown increasingly popular. This study provides a comprehensive review of the existing data mining techniques for making predictions on petroleum prices. The data mining techniques are classified into regression models, deep neural network models, fuzzy sets and logic, and hybrid models. A detailed discussion of how these models are developed and the accuracy of the models is provided.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
