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记忆性特征驱动的成品油价格预测研究

张金岱   

  1. 哈尔滨工程大学经济管理学院, 哈尔滨 150001
  • 收稿日期:2022-04-06 修回日期:2022-05-03 出版日期:2022-05-25 发布日期:2022-07-23

张金岱. 记忆性特征驱动的成品油价格预测研究[J]. 系统科学与数学, 2022, 42(5): 1300-1313.

ZHANG Jindai. Memory-Trait-Driven Refined Oil Price Forecasting[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(5): 1300-1313.

Memory-Trait-Driven Refined Oil Price Forecasting

ZHANG Jindai   

  1. School of Economics and Management, Harbin Engineering University, Harbin 150001
  • Received:2022-04-06 Revised:2022-05-03 Online:2022-05-25 Published:2022-07-23
为使成品油企业能够合理地进行生产、销售和库存决策,必须要先对成品油的市场价格做出准确的预测.然而,成品油价格由于受产销存等多种因素影响,使得成品油价格预测十分困难.为此,根据成品油价格数据自身的记忆性特征,文章构建了一个记忆性特征驱动的分解集成预测模型,并采用两种常见的成品油种------93#汽油和0#柴油的价格数据来验证该模型的有效性.实证结果证实基于记忆性特征驱动的分解集成预测模型能够获得比单模型更好的预测效果,表明该模型可作为预测类似成品油价格这种具有记忆性特征序列的有效工具.
In order to make reasonable production-sales-stock decision making for refined oil enterprises, it is necessary to make an accurate prediction of the refined oil price. However, the refined oil price is often affected by many factors such as production supply, market demand and product stock of refined oil products, which makes it very difficult to predict the trend of refined oil price. Therefore, this paper constructs a memory-trait-driven decomposition-ensemble forecasting model in terms of memory characteristics of refined oil price data. Meantime, in order to verify the effectiveness of the proposed model, the price data of two common refined oil products-93# gasoline and 0# diesel are used. The empirical results show that the proposed memory-trait-driven decomposition-ensemble forecasting model can achieve better prediction results than the single models, indicating that the proposed methodology can be used as an effective tool to predict the refined oil price series with memory traits.

MR(2010)主题分类: 

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