基于多模式特征聚合的未来商业预测
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Future Business Forecasting Based on Multi-mode Feature Aggregation
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    摘要:

    准确预测商业销售量未来趋势对于企业开发经营、政府宏观调控等至关重要. 传统的数据预测方法计算时间开销大, 具有主观性, 而现有基于数据驱动的未来商业预测方法没有考虑到数据集中的特征多样. 商业销售量数据是一个时序数据, 时序数据中包含了丰富的时间窗特征、滞后历史特征和价格变化趋势特征等众多特征, 先前的研究往往只注重于其中的某些特征, 对于特征的融合和增强探究偏少, 现有的未来商业预测方法的预测精度仍然有待提高. 为此, 本文提出了一种基于多模式特征聚合的未来商业预测方法, 该方法首先将商业销售量数据进行预处理; 然后基于特征工程提取数据集的5组不同的时间窗特征和其他特征; 在机器学习上对于5组时间窗特征采用硬投票机制选择合适的模型训练, 同时也采用神经网络的优化模型提取时序特征和预测结果, 然后分析销售量数据集和某些特征之间的依赖关系; 最后基于软投票模型完整地模型融合实现了商业销售量的高精度预测. 一系列实验结果表明, 本文提出的方法具有较高预测精度和效率, 明显优于现有预测方法.

    Abstract:

    Accurate prediction of the future trend of the commercial sales volume is of great importance to the development and operation of enterprises and the macro-control by the government. Traditional data prediction methods are time-consuming and subjective, while the existing data-driven future business prediction methods do not take into account the diversity of features in the data sets. The data of the commercial sales volume is time-series, which contains a wealth of time window features, lagging historical features, and price change trend features. Previous studies tend to focus only on some of these features, and the integration and enhancement of these features are seldom explored. The prediction accuracy of the existing future business prediction methods still needs to be improved. Therefore, this study proposes a future business forecast method based on multimodal feature aggregation, which firstly preprocesses the commercial sales volume data and then extracts five different groups of time window features and other features of the data set on the basis of feature engineering. In machine learning, the hard voting mechanism is used to select the appropriate model for the training of the five groups of time window features. At the same time, the neural network optimization model is applied to extract the time-series features and forecast results, and then, the dependency relationships between the data set of the sales volume and some features are analyzed. Finally, with the soft voting model, a high-precision forecast of the commercial sales volume is achieved by complete model integration. The experimental results reveal that the proposed method has high prediction accuracy and efficiency, which is greatly better than the existing prediction methods.

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崔铭浩,张仁博,郭恩铭.基于多模式特征聚合的未来商业预测.计算机系统应用,2023,32(2):25-33

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  • 收稿日期:2022-06-01
  • 最后修改日期:2022-07-01
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  • 在线发布日期: 2022-10-28
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