Sale forecasting method based on big data mining of cloud service platform

Abstract

The invention discloses a sale forecasting method based on big data mining of a cloud service platform. The method specifically includes the step of conducting data mining analysis according to data of historical successful goods distribution of a warehouse, forecasting a prospective sale condition and forecasting the sale quantity and the growth rate according to historical sale data of the goods. According to the sale forecasting method based on the big data mining of the cloud service platform, sale data of existing orders and goods distribution data of the warehouse are analyzed and forecasted; the method is simple, efficient and capable of meeting actual demands.

Description

technical field [0001] The invention belongs to electronic information technology, relates to big data and cloud computing applications in e-commerce, in particular to a sales prediction method based on cloud service platform big data mining. Background technique [0002] With the development of market economy and economic globalization, enterprises are facing increasingly fierce market competition. If an enterprise wants to win the competition and obtain customers, it must provide products to customers at the lowest cost in the fastest time, which makes accurate and timely product sales forecast and the resulting reliable decision-making become the key to the success of modern enterprises. The key factor, from which some sales forecasting systems also came into being. With the development of computer technology, network technology, communication technology and Internet technology and the automation of various business operation processes, enterprises have generated tens or...

AI Technical Summary

Problems solved by technology

Prediction involves the processing of massive data, and traditional methods cannot meet the requirements of operating efficiency, computing performance, accuracy and storage space

The data required for prediction contains a lot of incomplete (missing attribute values ​​​​or only contain aggregated data), noisy (errors or outlier values ​​that deviate from expectations), inconsistent content (from multiple data sources or differences in encoding), messed up forecasts

Traditional database technology is relatively weak in the expression, synthesis and reasoning of predictive knowledge, and it is difficult to meet the increasing forecasting requirements

Images