Method for predicting material purchasing demand based on GRU network

Abstract

The invention discloses a method for predicting material purchasing demand based on a GRU network. The method comprises the following steps: S1, carrying out data processing on pre-collected data by adopting a preset method; S2, comparing the original prediction model with the recurrent neural network model through a preset method; and S3, constructing a GRU model by adopting a preset method, andtaking the processed data as the input of the GRU model to obtain a predicted value. The method has the beneficial effects that the GRU model is established, so that training and updating parameters are reduced, the structure of the GRU model is simpler, the training time is greatly reduced, the article purchase prediction model is optimized, and the prediction accuracy is improved.

Description

technical field [0001] The present invention relates to the technical field of methods for forecasting demand for material procurement, and in particular, relates to a method for forecasting demand for material procurement based on a GRU network. Background technique [0002] With the rapid development of my country's social economy, resource management and resource allocation have been deeply rooted in the hearts of the people, and resources are the foundation of the company's growth. Without sufficient advantageous resources, it is difficult for an enterprise to develop. If an enterprise is not clear about its own resource composition, it will not be able to know itself and the enemy, and it is impossible to win in the competition. On the contrary, if you are very clear about your own resource composition and the resource composition of your competitors, you can accurately judge various situations. Therefore, we must use and allocate resources reasonably. [0003] Takin...

AI Technical Summary

Problems solved by technology

[0005] However, the existing demand forecasting model for procurement materials has the following defects: 1. Data redundancy, discrete, large fluctuations in item purchases, no data cleaning for procurement data, ignoring the impact of outliers on forecasting; 2. Procurement There are too many factors affecting demand, and the ordinary linear regression model cannot fit the prediction function well; 3. The time series relationship between historical purchases is not considered, and the prediction accuracy is low

In addition, the existing forecasting model takes a long time to train, requires a large amount of parameters for training, and has a complex structure

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