Design And Implementation Of Passenger Car Sales Forecast System Based On Integrated Learning

The era of fast increase of the Chinese economy has transformed the betterment of the quality stage.It is at an unprecedented critical stage,and profit at this stage cannot be achieved without the efforts of government,business and people.The Chinese government has published relevant economic development documents and adhered to companies and limited people,the Chinese economy has overcome numerous internal and external pressures and entered a critical period to change past development.The automotive industry is one of the sectors that affect China’s economic development.At this stage,companies have the trouble of production surplus and mismatch between supply and demand.In particular,the overall overcapacity in the passenger car market is becoming increasingly serious and the passenger car market has begun to move towards micro-growth.Therefore,more accuracy is required in passenger car sales forecasts.The data obtained with an accurate forecast will also become an important benchmark for future vehicle production.This article adopts market segmentation strategies for passenger car sales forecasts.Based on an in-depth study of machine learning and other related technologies,this article provides a comparative analysis of vehicle demand,economic impacts,and traffic conditions in different markets to find market demand and shape marketing.The advantage of decision making is to design a car ovellsell prediction system based on an integrated learning model.The specific content of the study is as follows:(1)This document uses as baseline data on the historical behavior of 60 models in 22 provinces for 28 consecutive months and analyzes the data from a sales impact perspective and pre-processes the data.(2)This article first transforms the time series trouble into a regression problem for supervised learning using the sliding window method.A deep mine is then made for the original data set,drawing design is performed from the perspective of province,month,and vehicle body,and the feature is expanded to 300 dimensions.To prevent the occurrence of the model over-fit phenomenon,the Xgboost algorithm is used to evaluate and screen the significance of the properties;(3)Based on the three popular integrated Catboost,Xgboost,and Lightgbm learning algorithms,this article uses the Stacking method to build a fusion model.(4)Based on the passenger car segment sales forecast model,this document designs and implements a number of sales forecasting systems using technologies related to the passenger car segment market.The system is aimed primarily at groups such as manufacturers and distributors,and provides analysis of historical data and accurate forecasting functions for these types of user groups.This article mainly solves the trouble of capacity in the surplus car market by taking advantage of the automotive user purchase demand for cars and extracting past data on passenger car behavior for consumer demand.Changing in this market It’s about predicting development.In today’s domestic and foreign research areas,there are few research results on data mining problems in the passenger car market segments.The research in this article provides a solution that can help companies create a market-oriented business development model in fierce market competition,reduce the loss of the business.