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Intelligent sales volume forecasting using Google search engine data

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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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Acknowledgements

The author thanks the National Science Council of Taiwan, ROC for financially supporting this research under contract NSC101-2410-H-155-004. This manuscript was edited by Wallace Academic Editing.

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  1. Department of Information Management, Innovation Center for Big Data and Digital Convergence, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taoyuan, 32003, Taiwan, ROC

    Fong-Ching Yuan & Chao-Hui Lee

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  1. Fong-Ching Yuan
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  2. Chao-Hui Lee
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Correspondence to Fong-Ching Yuan.

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Yuan, FC., Lee, CH. Intelligent sales volume forecasting using Google search engine data. Soft Comput 24, 2033–2047 (2020). https://doi.org/10.1007/s00500-019-04036-w

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