Macroeconomic situation is the overall performance of a country’s and regional economic situation. At present, the vast majority of macroeconomic indicators are obtained through sampling surveys, step-by-step reporting, statistical calculations, and other processes, which are publicly released by the Statistical Bureau. There are some shortcomings, such as lag and non-authenticity. Timely forecasting and early warning of macroeconomic trends are the important needs of government affairs. However, the timeliness of data has a direct impact on government decision-making. In this paper, the high frequency and relatively accurate big data sources are adopted to construct a multivariate regression prediction model for traditional national economic accounting indicators (such as industrial value added above the scale of Hefei), which is different from the traditional time series prediction model such as ARIMA model. Based on the macroeconomic prediction model of time series big data, multi-latitude data sources, sequential update, verification set screening model and other strategies are used to provide more reliable, timely, and easy-to-understand forecasting values of national economic accounting indicators. At the same time, the potential influencing factors of macroeconomic indicators are excavated to provide data and theoretical basis for macroeconomic analysis and decision-making.

中文翻译：

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基于大数据的顺序宏观经济预测工业增加值

宏观经济形势是一个国家和地区经济形势的总体表现。目前，绝大多数宏观经济指标是通过统计局公开发布的抽样调查，逐步报告，统计计算和其他程序获得的。有一些缺点，例如滞后和非真实性。及时预测和预警宏观经济趋势是政府事务的重要需求。但是，数据的及时性直接影响政府的决策。本文采用高频，相对准确的大数据源，构建传统国民经济核算指标（如合肥市规模以上工业增加值）的多元回归预测模型，与ARIMA模型等传统时间序列预测模型不同。在时间序列大数据的宏观经济预测模型的基础上，利用多纬度数据源，顺序更新，验证集筛选模型等策略，为国民经济核算指标提供了更可靠，及时，易懂的预测值。同时，挖掘了宏观经济指标的潜在影响因素，为宏观经济分析和决策提供数据和理论依据。以及易于理解的国民经济核算指标预测值。同时，挖掘了宏观经济指标的潜在影响因素，为宏观经济分析和决策提供数据和理论依据。以及易于理解的国民经济核算指标预测值。同时，挖掘了宏观经济指标的潜在影响因素，为宏观经济分析和决策提供数据和理论依据。