工业工程

工业工程 ›› 2024, Vol. 27 ›› Issue (1): 86-95,127.doi: 10.3969/j.issn.1007-7375.220145

• 系统建模与优化 • 上一篇    下一篇

基于小波分解和ARIMA-GARCH-GRU组合模型的制造业PMI预测

陆文星1,2, 任环宇1, 梁昌勇1,2, 李克卿1   

  1. 1. 合肥工业大学 管理学院;
    2. 过程优化与智能决策教育部重点实验室，安徽 合肥 230009
  • 收稿日期:2022-07-31 发布日期:2024-03-05
  • 作者简介:陆文星 (1971—)，男，江苏省人，副教授，主要研究方向为信息管理和信息系统、数据分析
  • 基金资助:
    国家自然科学基金资助项目 (72131006)

Manufacturing PMI Forecasting Based on Wavelet Decomposition and a ARMA-GARCH-GRU Combination Model

LU Wenxing1,2, REN Huanyu1, LIANG Changyong1,2, LI Keqing1   

  1. 1. School of Management;
    2. Key Laboratory of Process Optimization & Intelligent Decision-Making of Ministry of Education, Hefei University of Technology, Hefei 230009, China
  • Received:2022-07-31 Published:2024-03-05

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摘要： 制造业采购经理人指数 (PMI) 是反映国家经济运行情况的重要指标，而传统预测模型对该类时序数据预测精度不高。针对制造业PMI指数的非线性、波动性和数据量少的特点，提出一种基于一维离散小波变换进行数据预处理的组合模型。时序数据经过小波变换，由整合移动平均自回归–广义自回归条件异方差模型 (ARIMA-GARCH) 处理稳态低频数据，门控循环单元 (GRU) 处理波动性强的高频数据，将各频段预测结果进行融合得到最终预测结果。为验证模型有效性，选取一定数据量的PMI指数进行实验。结果表明，与其他常见模型对比，本文构建的组合模型具有较好的预测精度与性能，平均绝对误差 (MAE) 、均方根误差 (RMSE) 、平均绝对百分比误差 (MAPE) 分别达到0.003 29、 0.004 162、0.65%。

关键词: 采购经理人指数 (PMI), 小波分解, 整合移动平均自回归模型 (ARIMA), 广义的自回归条件异方差模型 (GARCH), 门控循环单元 (GRU)

Abstract: The Manufacturing Purchasing Managers Index (PMI) is an important indicator in the manufacturing industry reflecting the performance of a country’s economy. However, traditional forecasting models have low accuracy for predicting such time series data. Focusing on the characteristics of non-linearity, volatility and limited data volume of PMI index in the manufacturing industry, a combined model based on one-dimensional discrete wavelet transform for data preprocessing is proposed. After the wavelet transform of time-series data, steady-state low-frequency data are processed by an auto regressive moving average – generalized autoregressive conditional heteroscedasticity model (ARMA-GARCH), while the gated recurrent unit (GRU) handles high-frequency data with strong volatility. The prediction results of each frequency band are combined to get final prediction result. In order to verify the effectiveness of the model, a certain amount of data with PMI indices is selected for experiments. Results show that, compared with other common models, the combined model established in this paper has better prediction accuracy and performance, where the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) reach 0.00 329, 0.004 162, 0.65%.

Key words: purchasing managers index (PMI), wavelet decomposition, wauto regressive moving average model (ARIMA), generalized autoregressive conditional heteroscedasticity model (GARCH), gated recurrent unit (GRU)

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