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A Neural Network-Based Forecasting Model for Univariate Sales Forecasting

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Intelligent Decision-making Models for Production and Retail Operations

Abstract

This chapter addresses the time series forecasting performance of sparsely connected neural networks (SCNNs). A novel type of SCNNs is presented based on the Apollonian networks. In terms of three types of publicly available benchmark data, a series of experiments are conducted to compare the forecasting performance of the proposed SCNNs, randomly connected SCNNs and traditional feed-forward neural networks. The comparison results show that the proposed networks generate the best time series forecasting performance and the traditional networks generate the worst in terms of training speed and forecasting accuracy. The performance of the proposed SCNNs is evaluated further based on different training sample sizes and training accuracy measures. The experimental results indicate that larger training sample sizes do not necessarily give better forecasts while forecasts based on training accuracy measures, MAD and MAPE, are generally superior to those based on MSE and MASE.

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Author information

Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, China

    Zhaoxia Guo PhD

Authors
  1. Zhaoxia Guo PhD
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Corresponding author

Correspondence to Zhaoxia Guo PhD .

Appendices

Appendix A Experimental Results of Experiment 1

See Tables 10.4, 10.5 and 10.6.

Table 10.4 Comparison results of time series forecasting of ASNNs, RSNNs, and TFNNs (Microdata)
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Table 10.5 Comparison results of time series forecasting of ASNNs, RSNNs, and TFNNs (finance data)
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Table 10.6 Comparison results of time series forecasting of ASNNs, RSNNs, and TFNNs (tele data)
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Appendix B Experimental Results of Experiment 2

See Tables 10.7, 10.8 and 10.9.

Table 10.7 Experimental results of time series forecasting based on different training sample sizes (microdata)
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Table 10.8 Experimental results of time series forecasting based on different training sample sizes (finance data)
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Table 10.9 Experimental results of time series forecasting based on different training sample sizes (tele data)
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Appendix C Experimental Results of Experiment 3

See Tables 10.10, 10.11 and 10.12.

Table 10.10 Experimental results of time series forecasting based on different accuracy measures (microdata)
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Table 10.11 Experimental results of time series forecasting based on different accuracy measures (finance data)
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Table 10.12 Experimental results of time series forecasting based on different accuracy measures (tele data)
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Guo, Z. (2016). A Neural Network-Based Forecasting Model for Univariate Sales Forecasting. In: Intelligent Decision-making Models for Production and Retail Operations. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-52681-1_10

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  • DOI: https://doi.org/10.1007/978-3-662-52681-1_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-52679-8

  • Online ISBN: 978-3-662-52681-1

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