A Rough-Set-Refined Text Mining Approach for Crude Oil Market Tendency Forecasting
A Rough-Set-Refined Text Mining Approach for Crude Oil Market Tendency Forecasting
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发表时间：
2005
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通讯作者：
Lean Yu;Shouyang Wang;K. Lai
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作者：
Lean Yu;Shouyang Wang;K. Lai
在这项研究中，我们提出了一个基于知识的预测系统-粗糙集-精炼文本挖掘（RSTM）的方法-原油价格趋势预测。该系统由两个模块组成。在第一个模块中，使用文本挖掘技术，通过提取非结构化文本文档，构建元数据库和生成粗糙知识，包括收集各种相关文本文档，预处理文档，特征提取，元数据挖掘和粗糙知识生成。第二个模块采用粗糙集理论作为粗糙知识的知识精炼器，包括信息表的建立、信息约简和粗糙知识的精炼。通过将这两个部分结合起来，产生了一些有用的模式和规则（“知识”），这些模式和规则可用于原油市场趋势预测。为了评估RSTM的预测能力，我们将其性能与传统方法（例如，统计模型和时间序列模型）和神经网络模型。实证结果表明，RSTM优于其他预测模型，并表明所提出的方法是适合于同时应用到广泛的实际预测问题下的不确定性。此外，实验结果表明，我们提出的方法是一个很有前途的替代传统的原油市场趋势预测方法。
In this study, we propose a knowledge-based forecasting system — rough-set-refined text mining (RSTM) approach — for crude oil price tendency forecasting. This system consists of two modules. In the first module, text mining techniques are used to construct a metadata repository and generate rough knowledge by extracting unstructured text documents, including gathering various related text documents, preprocessing documents, feature extraction, and metadata mining and rough knowledge generation. In the second module, rough set theory is used as a knowledge refiner for the rough knowledge, which includes information table formulation, information reduction and rough knowledge refinement. By combining these two components, some useful patterns and rules (“knowledge”) are generated, which can be used for crude oil market tendency forecasting. To evaluate the forecasting ability of RSTM, we compare its performance with that of conventional methods (e.g., statistical models and time series models) and neural network models. The empirical results reveal that RSTM outperforms other forecasting models and demonstrate that the proposed approach is suitable for simultaneous application to a wide range of practical prediction problems under uncertainty. In addition, experimental results reveal that our proposed approach is a promising alternative to the conventional methods for crude oil market tendency forecasting.