Coal sales volume data prediction method and device, and medium

A technology for data forecasting and sales volume, applied in the field of data analysis, can solve the problem that the forecasting algorithm cannot accurately reflect the seasonal cyclical changes of coal sales volume, and the accuracy is not high, so as to ensure the value, improve the accuracy, and improve the degree of fitting. Effect

Pending Publication Date: 2022-01-21
浪潮卓数大数据产业发展有限公司
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Problems solved by technology

[0003] The embodiment of this application provides a coal sales data forecasting method, equipment and medium to solve the technical proble

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  • Coal sales volume data prediction method and device, and medium
  • Coal sales volume data prediction method and device, and medium
  • Coal sales volume data prediction method and device, and medium

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[0025] In order to make the objects, technical solutions and advantages of the present application, the technical solutions of the present application will be described in conjunction with the specific embodiments and corresponding drawings of the present application. Obviously, the described embodiments are merely the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, one of ordinary skill in the art is in the scope of the present application without making creative labor premistence.

[0026] The technical solution proposed in the present application embodiment will be described in detail below by the drawings.

[0027] figure 1 A flowchart of a coal sales data prediction method provided in the present application embodiment. like figure 1 As shown, a coal sales data prediction method provided by the present application embodiment can preferably include the following steps:

[0028] S101: The server gets the ...

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Abstract

The invention discloses a coal sales data prediction method and device and a medium, and is used for solving the technical problems that an existing prediction algorithm cannot accurately reflect seasonal periodic changes of coal sales and is not high in accuracy. The method comprises the steps of obtaining historical coal sales volume data of an enterprise from a database, and performing missing value filling on the historical coal sales volume data through an exponential smoothing algorithm to obtain continuous coal sales volume time sequence data; performing stationary processing on the coal sales time sequence data to obtain stationary time sequence data; according to an autocorrelation graph and a partial autocorrelation graph, estimating model parameters of a difference integration moving average autoregression model so as to obtain a product season model used for estimating the coal sales volume data; inputting the stationary time sequence data into the product season model for model fitting, and adjusting model parameters; and on the basis of the product season model after model parameter adjustment, predicting coal sales volume data.

Description

technical field [0001] This application relates to the technical field of data analysis, in particular to a method, equipment and medium for predicting coal sales data. Background technique [0002] Coal sales data is of vital significance to the procurement plan designation, inventory management, capital turnover, production decision-making, etc. of energy companies. Manual forecasting is often difficult to grasp the accurate trend changes of the data, and it is highly subjective. The market experience of decision-makers , Strategic vision, etc. have higher requirements. Therefore, data forecasting algorithms have developed rapidly in recent years, and the coal industry has special seasonal characteristics, but ordinary data forecasting algorithms do not consider the trend and seasonal factors in the time series, and the model fits the data poorly. Low, which makes the forecast of coal sales data inaccurate. Moreover, when the acquired data has missing values, common data...

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Application Information

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IPC IPC(8): G06Q30/02G06F17/18
CPCG06Q30/0202G06Q30/0201G06F17/18
Inventor 张通张安举崔乐乐
Owner 浪潮卓数大数据产业发展有限公司
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