Research And Application Of Sales Forecasting Method For Mechanical Transmission Parts Manufacturing Enterprises Based On ARIMA-LSTM Hybrid Model

With the progress of manufacturing technology and the diversification of customer demand,product sales volume prediction is very important for enterprises in the small batch and multi variety production mode.Through the effective and accurate sales forecast,the enterprise manager can arrange the production more effectively on the basis of the forecast results,distribute the product stock reasonably,respond to the customer’s demand and improve the customer satisfaction in real time,so as to win the customer and occupy the market.Although many models and methods have been used for sales prediction,many methods and models are aimed at specific problems and specific areas,and there are few methods and models that are effective and universal for all forecasting problems.From the standpoint of enterprises,we urgently want to build a model with high prediction accuracy and adaptability,which is used to predict future demand of products.This paper has done the following research work.Research and comparison of the research status of sales forecast at home and abroad,combined with the characteristics of traditional manufacturing enterprises,analyzes the feasibility of traditional sales forecasting methods and models.The idea of ARIMA model is to predict future data by observing historical data.In sales forecasting,it is based on product history sales volume to predict future sales volume.This method can not be used to analyze many factors affecting product sales.It is only concerned with historical data,which can be applied in many scenarios,but ARIMA is a linear model that can not fit the nonlinear components of complex time series and the prediction accuracy is not high.In this paper,the solution to the above problems is from the perspective of time series analysis.Based on the ARIMA model,we use the hybrid model to improve the prediction accuracy.Based on the idea of predecessor hybrid model,we combine deep learningl network LSTM with traditional ARIMA model.The hybrid model can integrate the characteristics of different models.With the help of the excellent characteristics of LSTM model,we can make up for the shortage of linear ARIMA model and improve the prediction accuracy.The traditional model mixing method assumes that the linear component and the nonlinear component of the time series are simple addition and relation.This simplification has a certain risk,which may lead to the reduction ofprediction accuracy.Therefore,this paper discarded the hypothesis of the traditional model mixed mode without assuming the relationship between the two,but used the self-learning ability of the LSTM neural network to describe the complex relationship between the two.In theory,this model is more scientific and reasonable,and the model built should have higher prediction accuracy.The advantages and disadvantages of the hybrid method and the traditional model method are verified by experiments.Four groups of experiments are set up and two indexes of average absolute error(MAE)and average absolute percentage error(MAPE)are selected to compare the single model and mixed model respectively.The hybrid model constructed in this way and the hybrid model constructed in the traditional way are shown in the prediction accuracy,and the construction of the mixed model is demonstrated.The superiority of the style.Finally,following the idea of software engineering,a sales forecasting system based on hybrid model is designed and implemented.