| 运用加权马尔科夫模型预测我国PMI |
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| 引用本文： | 舒服华.运用加权马尔科夫模型预测我国PMI[J].唐山学院学报,2020,33(3):77-83. |
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| 作者姓名： | 舒服华 |
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| 作者单位： | 武汉理工大学 继续教育学院, 武汉 430070 |
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| 基金项目： | 湖北省自然科学基金项目（2018CFB271） |
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| 摘 要： | 研究我国PMI的变化趋势，对观察宏观经济运行状态、制定相应的政策和措施、指导企业生产经营、促进国民经济健康发展都具有重要意义。针对传统马尔科夫预测模型存在对历史数据的作用均衡看待的弊端和预测结果比较笼统的问题，通过对不同时期的历史数据设置权重以及采用模糊数学处理预测结果的方法对模型进行改进，然后应用改进后的模型对我国PMI进行预测，得到2019年4月我国PMI为50.365。
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| 关 键 词： | 采购经理人指数 马尔科夫预测模型 权重 模糊数学 |
Forecast of PMI in China Based on Weighted Markov Model |
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| SHU Fu-hua.Forecast of PMI in China Based on Weighted Markov Model[J].Journal of Tangshan College,2020,33(3):77-83. |
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| Authors: | SHU Fu-hua |
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| Institution: | School of Continuing Education, Wuhan University of Technology, Wuhan 430070, China |
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| Abstract: | Studying the development trend of PMI in China is of great significance for observing the macroeconomic operation state, formulating corresponding policies and measures, guiding the production and operation of enterprises, and promoting the healthy development of national economy. Aiming at the problems that traditional Markov forecast model has a balanced view of historical data and a general forecast result, this method sets the weight for the historical data in different periods and improves the model by dealing with the forecast result through the fuzzy mathematics. Then, PMI in China is forecast with the improved model, with a forecast figure 50.365 in April of 2019. |
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