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08-05-2019 | Methodologies and Application

Intelligent sales volume forecasting using Google search engine data

Authors: Fong-Ching Yuan, Chao-Hui Lee

Published in: Soft Computing | Issue 3/2020

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Abstract

Business forecasting is a critical organizational capability for both strategic and tactical business planning. Improving the quality of forecasts is thus an important organization goal. In this paper, the intelligent sales volume forecasting models are constructed using grey analysis, deep learning (DNN), and least-square support vector regression (LSSVR) optimized through particle swarm optimization or genetic algorithm. First, features (predictors) from economic variables are extracted through grey analysis. The selected features together with Google Index, an exogenous variable used widely by researchers, are then used as the inputs to the DNN and LSSVR to build the models. The experimental results indicate that the grey DNN model, an emerging and pioneering artificial intelligence technology, can accurately predict sales volumes based on non-parametric statistical tests. DNN also outperformed the competing models when using Google Index.

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Title: Intelligent sales volume forecasting using Google search engine data
Authors: Fong-Ching Yuan
Chao-Hui Lee
Publication date: 08-05-2019
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 3/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04036-w

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