Product order prediction method and device with time series characteristics

Abstract

The invention discloses a product order prediction method and device with time series characteristics. The product order prediction method comprises obtaining statistics of order data of enterprises at every time point according to stored historical order data; selecting a prediction model according to the time series characteristics of the order data and determining a prediction output equation of the prediction model; enabling the statistics of the historical order data to be processed as a prediction input table according to the requirements of the prediction model and training a corresponding prediction network model; and utilizing the prediction input table of the prediction order quantity to calculate to obtain the prediction order quantity of orders according to the prediction model which is well-trained through prediction orders and the prediction output equation. The invention also provides the product order prediction device according to the product order prediction method. The product order prediction device mainly comprises a data acquisition module, a data preprocessing module, a time series modeling module and an order prediction module. The product order prediction method and device with the time series characteristics have the advantages of solving the nonlinear problem of product order prediction, meeting the requirements of system real-time performance and improving the prediction accuracy.

Description

technical field [0001] The invention belongs to the field of enterprise production management, and in particular relates to a product order prediction method and device with time series characteristics. Background technique [0002] Order forecasting is one of the important contents of modern enterprise production management. The modern economy is a fierce market economy, and the needs of customers in the market tend to be individualized and diversified, which puts forward new requirements for the production and management models and management methods of enterprises. Therefore, the order model representing customer demand becomes the source and end point of enterprise management and supply chain management. The modern market has changed from a seller's market to a buyer's market. Therefore, whether to capture as many customers as possible becomes the key to the survival and competitiveness of enterprises. The key to grabbing customers lies in whether the products can meet...

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Problems solved by technology

[0004] At present, there are many methods of order forecasting. Judging from the current research methods, most of them are based on data mining methods. These methods have limitations of complex algorithms in the production and application process, and lack of visual exploration of data before data prediction. At the same time, the advantages of applying to dynamic data analysis are not obvious, and cannot reflect the development trend of dynamic data

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