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 problem that the existing forecasting algorithm cannot accurately reflect the seasonal periodic changes of coal sales and the accuracy is not high

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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 purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and corresponding drawings. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0026] The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0027] figure 1 It is a flow chart of a coal sales data forecasting method provided in the embodiment of this application. Such as figure 1 As shown, a method for predicting coal sale...

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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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