Business data prediction method, device, equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and provides a business data prediction method, device, equipment and storage medium, which are used to improve the accuracy of business data prediction. The business data prediction method includes: data cleaning and dimension value division of business order data and vehicle sales data crawled by web crawlers to obtain new car data and old car data; Screening and merging to obtain the forecast dimension; through the preset forecast model, forecast dimension and preset forecast index, the business data of the new car data and the old car data in the preset period are predicted respectively, and the new car forecast data and the initial old car data corresponding to each dimension are obtained. Vehicle forecast data; the new car forecast data and the initial used car forecast data corresponding to each dimension are merged to obtain business forecast data. In addition, the present invention also relates to block chain technology, and new car data and old car data can be stored in the block chain.

Description

technical field [0001] The invention relates to the field of intelligent decision-making of artificial intelligence, in particular to a business data prediction method, device, equipment and storage medium. Background technique [0002] With the rapid increase of business volume, for the prospect of the scale and development trend of the business volume, it is necessary to make statistical predictions on various data of the business volume, for example: forecasting of business data. The current forecasting method of business data generally adopts the integrated moving average autoregressive model (autoregressive integrated moving average model, ARIMA), and predicts business data at a preset time according to the time series forecasting algorithm in the ARIMA model and historical business data. Obtain forecast business data. [0003] However, the forecasting method of the above-mentioned business data is to directly fuse the historical business data of different business sce...

AI Technical Summary

Problems solved by technology

[0003] However, the forecasting method of the above-mentioned business data is to directly fuse the historical business data of different business scenarios together for forecasting, and the historical business data of different business scenarios will have different prediction rules, such as: the premium charging standard for new cars and old cars Inconsistency, with the special nature of their respective business scenarios, resulting in a low degree of fit between the business data prediction model and the business scenario, and a high deviation rate of the predicted business data, resulting in low accuracy of business data prediction

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