工业工程2024，Vol.27Issue(1)：86-95,127,11.DOI:10.3969/j.issn.1007-7375.220145
基于小波分解和ARIMA-GARCH-GRU组合模型的制造业PMI预测
Manufacturing PMI Forecasting Based on Wavelet Decomposition and a ARMA-GARCH-GRU Combination Model
摘要
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 0.00329,0.004162,0.65%.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)Key words
purchasing managers index(PMI)/wavelet decomposition/wauto regressive moving average model(ARIMA)/generalized autoregressive conditional heteroscedasticity model(GARCH)/gated recurrent unit(GRU)分类
管理科学引用本文复制引用
陆文星,任环宇,梁昌勇,李克卿..基于小波分解和ARIMA-GARCH-GRU组合模型的制造业PMI预测[J].工业工程,2024,27(1):86-95,127,11.基金项目
国家自然科学基金资助项目(72131006) （72131006）
