Multi-assortment commodity price expectation data pre-processing method based on neural networks

Abstract

The invention discloses a multi-assortment commodity price expectation data pre-processing method based on neural networks. The best order of magnitude of commodity price data which is obtained from websites is calculated by an improved radical basis function (RBF) neural networks and an improved back propagation (BP) artificial neural networks. The calculated best order of magnitude is used to preprocess normalized order of magnitude of the commodity price. Expectation accuracy of the RBF neural networks and the BP neural networks is improved. Generality of the RBF neural networks and the BP neural networks for expectation of different kinds of commodity prices is improved.

Description

technical field [0001] The invention belongs to the field of data processing, in particular to a neural network-based data preprocessing method for multi-variety commodity price prediction, which can be applied to commodity price prediction data preprocessing in commodity price prediction analysis and commodity sales decision support systems. Background technique [0002] The commodity price prediction method is the basis of market forecast analysis and commodity production and sales decision-making. It is an important issue in the field of market forecasting and plays a key role in many problems such as commodity production and sales. The data preprocessing method It has a great influence on the generality and accuracy of the forecasting method. Due to the development of network technology and the popularization of online stores, people have paid more and more attention to the research of commodity price forecasting methods in recent years. The problem of commodity price f...

AI Technical Summary

Problems solved by technology

Among the improved prediction methods proposed, these prediction methods are highly pertinent and lack universality. The improved prediction methods are only applicable to one commodity or the same type of commodity, and the fixed parameters of the prediction method make the prediction method inflexible. , the accuracy of price prediction cannot be guaranteed when facing different commodities of the same category

The lack of flexibility and versatility makes these improved forecasting methods unable to meet the urgent needs of the vast number of sellers for market forecast analysis and commodity sales decisions of different types of commodities. Prediction methods for different commodity prices, or find a data preprocessing method for different types of commodity prices, in order to obtain better versatility and higher prediction accuracy of the prediction method

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