| 刘斌,董清浩.基于奇异谱分析的PMI组合预测模型[J].安徽建筑大学学报,2024,32(1):43-49 |
| 基于奇异谱分析的PMI组合预测模型 |
| PMI Combination Forecasting Model Based on Singular Spectrum Analysis |
| |
| DOI： |
| 中文关键词: PMI 奇异谱分析 SARIMA 支持向量回归 |
| 英文关键词: PMI SSA SARIMA SVR |
| 基金项目:安徽省高校省级自然科学研究项目（2022AH050247） |
|
| 摘要点击次数: 1230 |
| 全文下载次数: 0 |
| 中文摘要: |
| 为了提高制造业采购经理人指数（PMI）的预测精度，结合PMI周期性和非线性的特点，提出了融合奇异谱分析（SSA）、季节差分自回归移动平均（SARIMA）模型和支持向量回归（SVR）模型的组合预测模型。该模型采用SSA将PMI分解为主要成分和噪声成分，借助SARIMA模型处理线性问题以及SVR模型处理非线性问题的优势，分别为两个成分建立相应的预测模型，针对主要成分选取SARIMA模型和SVR模型建模，噪声成分选取SVR模型建模，最后将各自得到的结果组合为最终的预测结果。实验显示：SSA-SARIMA-SVR模型的误差评价指标最低，预测效果最好，可供预测PMI走势。 |
| 英文摘要: |
| To improve the forecasting accuracy of manufacturing purchasing managers' index (PMI), a model combining singular spectrum analysis (SSA), seasonal difference autoregressive moving average model (SARIMA) and support vector regression (SVR) was proposed, considering cyclicality and non-linearity characteristics of PMI. In this model, PMI was decomposed into major components and noisy components with SSA, and two forecasting models were established for two components separately. Due to the advantages of SARIMA model in handling the linear problem and SVR model in the non-linear problem, the major components were modeled using SARIMA and SVR models, and the noisy component using SVR model, hence, the forecasting results of two components were combined into the final forecasting. The final results showed that the SSA-SARIMA-SVR model had the lowest forecasting evaluation index and the best forecasting effect, indicating its application prospect in PMI forecasting. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
