论文标题
石油价格使用数据挖掘技术预测 - 审查
Petroleum prices prediction using data mining techniques -- A Review
论文作者
论文摘要
在过去的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。汇率不断转移的价格影响，这也影响了肯尼亚的商品工业产出。石油产品价格的任何变化都会显着影响其他物品的成本，甚至经济的扩张。因此，准确的石油价格预测对于制定适合抑制燃料相关冲击的政策至关重要。数据挖掘技术是用于查找数据中有价值模式的工具。石油价格预测中使用的数据挖掘技术，包括人工神经网络（ANN），支持向量机（SVM）以及诸如遗传算法（GA）之类的智能优化技术，越来越受欢迎。这项研究对现有数据挖掘技术进行了全面审查，以预测石油价格。数据挖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。关于如何开发这些模型以及提供模型的准确性的详细讨论。
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.
