Macroeconomic multi-source mixing big data modeling method
A technology of macroeconomics and modeling methods, applied in the field of macroeconomic multi-source mixed-frequency big data modeling and new models, can solve problems such as difficulty in result analysis and limitation of model explanatory variable dimensions, and achieve good interpretability and convenient analysis , the objective effect of modeling results
Inactive Publication Date: 2019-06-25
合肥黎曼信息科技有限公司
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[0008] The purpose of the present invention is to provide a macroeconomic multi-source mixing big data modeling method, through the modeling framework of data acquisition, feature analysis, training sample generation, model training, and result analysis step design, to solve the existing model interpretation The variable dimension has certain restrictions and the result analysis is difficult
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[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
[0043] see figure 1 As shown, the present invention is a macroeconomic multi-source mixing big data modeling method, comprising the following steps:
[0044] Step S1, expanding the response variable: expanding the response variable to obtain high-frequency response variable data;
[0045] Step S2, data acquisition: acquire mixed-frequency big data related to macroeconomic indicators from multiple sources;
[0046] Step S3, feature analysis: perform feature proces...
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The invention discloses a macroeconomic multi-source frequency mixing big data modeling method, and relates to the technical field of artificial intelligence. The method comprises the following steps:expanding a response variable, and providing a modeling basis for the addition of a high-frequency interpretation variable into modeling; carrying out feature processing on the acquired multi-sourcefrequency mixing big data, and eliminating the colinearity among variables and the interference of redundant variables on the model; according to the update lag time length of the explanation variableand the frequency of the response variable, determining the forward pushing time length of the explanation variable and the historical crossing time length to obtain a sample; and training and predicting the data by using a regression device to obtain an analysis result. According to the method, a response variable is expanded, and more high-frequency explanation variables are introduced, so thata modeling result with higher fine granularity is obtained; And multi-source mixing big data is introduced to construct a training sample training model, so that the modeling result is more objectiveand has better interpretability.
Description
technical field [0001] The invention belongs to the technical field of artificial intelligence, and in particular relates to a macroeconomic multi-source mixing big data modeling method. The method constructs a new model with higher fine-grainedness and is superior to the traditional macroeconomic index modeling method. Background technique [0002] Macroeconomic indicators measure a country's economic development level and reflect a country's economic development status. The modeling results of macroeconomic indicators have played a certain role in the future economic development planning of the region. However, at present, macroeconomic indicators are released by national or regional statistical bureaus, which are limited by traditional indicator calculation methods, and there are problems such as fewer release dimensions, lower frequency, and serious time lag. Therefore, it is difficult for these indicators to reflect the real situation of real macroeconomic development ...
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IPC IPC(8): G06F16/2458G06F16/2457G06F16/248G06Q10/06G06Q50/26
Inventor 宋艳枝孔京杨路
Owner 合肥黎曼信息科技有限公司