A method and a device for forecasting commodity inventory quantity

Abstract

The invention provides a method and a device for forecasting commodity stock quantity, which relates to the technical field of data analysis, and obtains target sample data for forecasting commodity stock quantity. Target sample data includes: training sample data; then the training sample data is input into the stochastic multi-scale kernel learning framework for training, and the optimal model parameters are obtained by cross-validation algorithm. The optimal model parameters are substituted into the stochastic multi-scale kernel function, and the stock forecasting model based on the stochastic multi-scale kernel function is obtained. The new input parameter data is inputted into the stock forecasting model based on the stochastic multi-scale kernel function, and the output result of thestock forecasting model is calculated as the commodity forecast stock corresponding to the new input parameter data. The invention can determine a stock quantity prediction model based on a random multi-scale kernel function through training sample data in a random multi-scale kernel learning framework and a cross-validation algorithm, so as to improve the accuracy and credibility of the stock quantity prediction result of commodities.

Description

technical field [0001] The present invention relates to the technical field of data analysis, in particular to a method and device for predicting commodity inventory quantity. Background technique [0002] The forecast method of commodity inventory 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 aspects such as commodity production and sales. [0003] In the existing inventory forecasting methods based on stochastic multi-scale kernel functions, the selection of kernels is entirely based on experience, and the resulting forecast models are sometimes good or bad, with low accuracy or poor generalization performance; in addition, when selecting After the kernel (basic function), for some multi-core algorithms, there is no automatic method to optimize the combination of these kernel functions, or even artificially set a combination of kernel func...

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Problems solved by technology

[0003] In the existing inventory forecasting methods based on stochastic multi-scale kernel functions, the selection of kernels is entirely based on experience, and the resulting forecast models are sometimes good or bad, with low accuracy or poor generalization performance; in addition, when selecting After the kernel (basic function), for some multi-core algorithms, there is no automatic method to optimize the combination of these kernel functions, or even artificially set a combination of kernel functions and optimize it. This calculation method is too complicated or The cost of optimization is too high, and sometimes, even with an optimized combination, the generalization performance of the model is not strong

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