Research And Application Of Fluorochemical Product Price Forecast Method

Product price prediction,as one of the important means of lean management and economic efficiency improvement of fluorine chemical enterprises,plays an important role in the business decision-making of enterprises.Faced with a large amount of data and complex influencing factors,traditional empirical estimation methods are gradually difficult to guarantee the accuracy of their predictions.Therefore,in the context of intelligent manufacturing,companies urgently need to improve their price forecasting capabilities and optimize their production decisions.This article takes a domestic fluorine chemical enterprise as the background,and conducts related research on the theory and method of fluorine chemical product price prediction.The main work and research contents of the thesis are as follows:(1)Aiming at the problems of insufficient data utilization and low prediction accuracy of fluorine chemical product price prediction methods,a LSTM price prediction model based on decomposition and reconstruction is proposed.The characteristic variables are selected through influencing factor analysis,and then the data is decomposed based on the ensemble empirical mode decomposition method(EEMD),the dynamic time warping algorithm(DTW)is used for clustering and reconstruction,and the LSTM model is established based on the reconstructed data for prediction,and finally integrated Get the predicted result.Experimental analysis shows that this method has a better prediction effect.(2)Aiming at the problems of error accumulation and weight attenuation in LSTM long-term price prediction,a multi-step period prediction model based on the GRU model,Seq2Seq structure and Attention mechanism is proposed to improve the accuracy of long-term prediction.At the same time,a production planning and scheduling model is established based on production constraints and objective functions,and price forecasts and production scheduling models are combined to optimize production scheduling plans.Finally,a case analysis verifies the effectiveness of the method(3)Aiming at the problem that the profitability analysis method of fluorine chemical products is single and imperfect,a multi-dimensional profitability analysis method based on interval prediction and multi-task learning is proposed.First,the prediction interval is estimated by the Bootstrap method,and then multi-task learning is combined with price and sales forecast tasks,and dynamic loss weights are used to improve the multi-task learning algorithm.Then,based on the results of interval and multi-task forecasting,a multi-dimensional profitability criterion library including time,quantity,and probability is established to analyze the profitability of the product.Case analysis shows that it can improve corporate profitability analysis capabilities.(4)Based on the price prediction algorithms and applications,with a fluorine chemical company as the object and application scenario,a set of fluorine chemical product price prediction and analysis software system was designed and developed based on the Django framework.The system includes functional modules such as data analysis,price prediction,and benefit optimization to assist enterprises in making decisions.Finally,based on the real data provided by the enterprise,the software system function experiment and the fluorine chemical example analysis are carried out.