人均GDP是国民经济核算的核心指标,也是衡量一个国家或地区经济状况和发展水平的重要指标。本文以Python作为数据分析工具,基于GM灰色系统模型对福清市未来的GDP进行了预测分析。通过收集福清市的2000至2022年度人均GDP数据,利用GM灰色...人均GDP是国民经济核算的核心指标,也是衡量一个国家或地区经济状况和发展水平的重要指标。本文以Python作为数据分析工具,基于GM灰色系统模型对福清市未来的GDP进行了预测分析。通过收集福清市的2000至2022年度人均GDP数据,利用GM灰色系统模型建立了预测模型。福清市作为福建省的重要经济节点,其人均GDP的预测对于了解闽东地区人均经济发展动态、对优化经济结构以及推动经济高质量发展具有重要意义。本文尝试了三种方式的灰色预测,通过对三种预测方式的指标和预测值之间比较,总结三种方法的特点,通解和优化解相较于邓聚龙模型,预测结果更为乐观,通解还提供了预测值的上下限范围,显示出灵活性和不确定性。邓聚龙模型则提供了较为保守的预测,主要基于历史数据的平均增长趋势。优化解通过更精细的算法和额外考虑因素进一步精确了预测值,并提供了多种预测方法的结果以增强可信度和可靠性。这三种差异反映了模型构建时考虑因素和假设条件的不同,本文旨在结合多个模型结果以获得更全面准确的预测分析。Per capita GDP is a core indicator of national economic accounting and an important indicator for measuring the economic status and development level of a country or region. This article uses Python as a data analysis tool and builds a prediction model based on the GM gray system model to predict the future GDP of Fuqing City. By collecting the per capita GDP data of Fuqing City from 2000 to 2022, a prediction model is established using the GM gray system model. As an important economic node in Fujian Province, the prediction of Fuqing City’s per capita GDP is of great significance for understanding the economic development dynamics of the eastern region of Fujian, optimizing the economic structure, and promoting high-quality economic development. This article attempts three types of gray prediction methods. By comparing the indicators and predicted values of the three prediction methods, the characteristics of the three methods are summarized. Compared with the Deng Julong model, the prediction results are more optimistic, and the general solution also provides the upper and lower limits of the predicted values, showing flexibility and uncertainty. The Deng Julong model provides a more conservative prediction, mainly based on the average growth trend of historical data. The optimized solution further refines the predicted values through more refined algorithms and additional considerations, and provides results from multiple prediction methods to enhance credibility and reliability. These three differences reflect the different factors and assumptions considered during model construction. This article aims to combine multiple model results to obtain a more comprehensive and accurate prediction analysis.展开更多
文摘人均GDP是国民经济核算的核心指标,也是衡量一个国家或地区经济状况和发展水平的重要指标。本文以Python作为数据分析工具,基于GM灰色系统模型对福清市未来的GDP进行了预测分析。通过收集福清市的2000至2022年度人均GDP数据,利用GM灰色系统模型建立了预测模型。福清市作为福建省的重要经济节点,其人均GDP的预测对于了解闽东地区人均经济发展动态、对优化经济结构以及推动经济高质量发展具有重要意义。本文尝试了三种方式的灰色预测,通过对三种预测方式的指标和预测值之间比较,总结三种方法的特点,通解和优化解相较于邓聚龙模型,预测结果更为乐观,通解还提供了预测值的上下限范围,显示出灵活性和不确定性。邓聚龙模型则提供了较为保守的预测,主要基于历史数据的平均增长趋势。优化解通过更精细的算法和额外考虑因素进一步精确了预测值,并提供了多种预测方法的结果以增强可信度和可靠性。这三种差异反映了模型构建时考虑因素和假设条件的不同,本文旨在结合多个模型结果以获得更全面准确的预测分析。Per capita GDP is a core indicator of national economic accounting and an important indicator for measuring the economic status and development level of a country or region. This article uses Python as a data analysis tool and builds a prediction model based on the GM gray system model to predict the future GDP of Fuqing City. By collecting the per capita GDP data of Fuqing City from 2000 to 2022, a prediction model is established using the GM gray system model. As an important economic node in Fujian Province, the prediction of Fuqing City’s per capita GDP is of great significance for understanding the economic development dynamics of the eastern region of Fujian, optimizing the economic structure, and promoting high-quality economic development. This article attempts three types of gray prediction methods. By comparing the indicators and predicted values of the three prediction methods, the characteristics of the three methods are summarized. Compared with the Deng Julong model, the prediction results are more optimistic, and the general solution also provides the upper and lower limits of the predicted values, showing flexibility and uncertainty. The Deng Julong model provides a more conservative prediction, mainly based on the average growth trend of historical data. The optimized solution further refines the predicted values through more refined algorithms and additional considerations, and provides results from multiple prediction methods to enhance credibility and reliability. These three differences reflect the different factors and assumptions considered during model construction. This article aims to combine multiple model results to obtain a more comprehensive and accurate prediction analysis.
