Skip to main content
SpringerLink
Log in

An Extreme Learning Machine-Based Intelligent Decision-Making Model for Multivariate Sales Forecasting

  • Chapter
  • First Online:
Intelligent Decision-making Models for Production and Retail Operations

Abstract

A sales forecasting problem in the retail industry is addressed based on early sales. An effective multivariate intelligent decision-making (MID) model is developed to provide effective forecasts for this problem by integrating a data preparation and preprocessing (DPP) module, a harmony search-wrapper-based variable selection (HWVS) module and a multivariate intelligent forecaster (MIF) module. The HWVS module selects out the optimal input variable subset from given candidate inputs as the inputs of MIF. The MIF is established to model the relationship between the selected input variables and the sales volumes of retail products and then utilized to forecast the sales volumes of retail products. Extensive experiments were conducted to validate the proposed MID model in terms of extensive typical sales datasets from real-world retail industry. Experimental results show that it is statistically significant that the proposed MID model can generate much better forecasts than extreme learning machine-based model and generalized linear model do.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Boulden, J. B. (1958). Fitting the sales forecast to your firm. Business Horizons, 1(1), 65–72.

    Article  Google Scholar 

  • Cao, Q., & Parry, M. (2009). Neural network earnings per share forecasting models: A comparison of backward propagation and the genetic algorithm. Decision Support Systems, 47(1), 32–41.

    Article  Google Scholar 

  • Carbonneau, R., Kersten, G., & Vahidov, R. (2011). Pairwise issue modeling for negotiation counteroffer prediction using neural networks. Decision Support Systems, 50(2), 449–459.

    Article  Google Scholar 

  • Chang, P., Wang, Y., & Tsai, C. (2005). Evolving neural network for printed circuit board sales forecasting. Expert Systems with Applications, 29(1), 83–92.

    Article  Google Scholar 

  • Chen, F. L., & Ou, T. Y. (2009). Gray relation analysis and multilayer functional link network sales forecasting model for perishable food in convenience store. Expert Systems with Applications, 36(3), 7054–7063.

    Article  Google Scholar 

  • Chen, F. L., & Ou, T. Y. (2011). Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Systems with Applications, 38(3), 1336–1345.

    Article  MathSciNet  Google Scholar 

  • Chern, C.-C., Ao, I. K. I., Wu, L.-L., & Kung, L.-C. (2010). Designing a decision-support system for new product sales forecasting. Expert Systems with Applications, 37(2), 1654–1665.

    Article  Google Scholar 

  • Chu, C., & Zhang, G. (2003). A comparative study of linear and nonlinear models for aggregate retail sales forecasting. International Journal of Production Economics, 86(3), 217–231.

    Article  Google Scholar 

  • De Gooijer, J., & Hyndman, R. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443–473.

    Article  Google Scholar 

  • Dobson, A. J., & Barnett, A. G. (2008). Introduction to generalized linear models. Chapman & Hall/CRC: Boca Raton, Florida, USA.

    Google Scholar 

  • Fildes, R., & Hastings, R. (1994). The organization and improvement of market forecasting. The Journal of the Operational Research Society, 45(1), 1–16.

    Article  Google Scholar 

  • Fisher, M. L., Raman, A., & McClelland, A. S. (2000). Rocket science retailing is almost here—Are you ready? Harvard Business Review, 78(4), 115–124.

    Google Scholar 

  • Gardner, E. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637–666.

    Article  Google Scholar 

  • Guo, Z. X., Wong, W. K., Leung, S. Y. S., & Li, M. (2011). Applications of artificial intelligence in the apparel industry: A review. Textile Research Journal, 81(18), 1871–1892.

    Article  Google Scholar 

  • Guo, Z., Wong, W., & Li, M. (2013). A multivariate intelligent decision-making model for retail sales forecasting. Decision Support Systems, 55(1), 247–255.

    Google Scholar 

  • Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.

    MATH  Google Scholar 

  • Hyndman, R., & Koehler, A. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.

    Article  Google Scholar 

  • Jacobi, M., Karimanzira, D., & Ament, C. (2007). Water demand forecasting using Kalman filtering. In Proceedings of the 16th IASTED International Conference on Applied Simulation and Modelling (pp. 199–202).

    Google Scholar 

  • Kohavi, R., & John, G. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1–2), 273–324.

    Article  MATH  Google Scholar 

  • Kotsiantis, S. B., Kanellopoulos, D., & Pintelas, P. E. (2006). Data preprocessing for supervised learning. International Journal of Computational Science, 1(2), 111–117.

    Google Scholar 

  • Lancaster, G., & Reynolds, P. (2002). Marketing: Made simple. Oxford, UK: Elsevier.

    Google Scholar 

  • Lo, T. (1994). An expert-system for choosing demand forecasting techniques. International Journal of Production Economics, 33(1–3), 5–15.

    Article  Google Scholar 

  • Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188(2), 1567–1579.

    Article  MathSciNet  MATH  Google Scholar 

  • Makridakis, S., & Hibon, M. (1997). ARMA models and the Box-Jenkins methodology. Journal of Forecasting, 16(3), 147–163.

    Article  Google Scholar 

  • Sakai, H., Nakajima, H., Higashihara, M., Yasuda, M., & Oosumi, M. (1999). Development of a fuzzy sales forecasting system for vending machines. Computers & Industrial Engineering, 36(2), 427–449.

    Article  Google Scholar 

  • Smith, P., Husein, S., & Leonard, D. (1996). Forecasting short term regional gas demand using an expert system. Expert Systems with Applications, 10(2), 265–273.

    Article  Google Scholar 

  • Sun, Z., Choi, T., Au, K., & Yu, Y. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1), 411–419.

    Article  Google Scholar 

  • Tanaka, K. (2010). A sales forecasting model for new-released and nonlinear sales trend products. Expert Systems with Applications, 37(11), 7387–7393.

    Article  Google Scholar 

  • Tang, Z., Dealmeida, C., & Fishwick, P. (1991). Time-series forecasting using neural networks vs Box-Jenkins methodology. Simulation, 57, 303–310.

    Article  Google Scholar 

  • Taylor, J. (2007). Forecasting daily supermarket sales using exponentially weighted quantile regression. European Journal of Operational Research, 178(1), 154–167.

    Article  MATH  Google Scholar 

  • Thomassey, S., & Fiordaliso, A. (2006). A hybrid sales forecasting system based on clustering and decision trees. Decision Support Systems, 42(1), 408–421.

    Article  Google Scholar 

  • Tseng, F. (2008). Quadratic interval innovation diffusion models for new product sales forecasting. Journal of the Operational Research Society, 59(8), 1120–1127.

    Article  Google Scholar 

  • Winters, P. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342.

    Google Scholar 

  • Wong, W. K., & Guo, Z. X. (2010). A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. International Journal of Production Economics, 128(2), 614–624.

    Article  Google Scholar 

  • Wong, W. K., Guo, Z. X., & Leung, S. Y. S. (2010). Partially connected feedforward neural networks on apollonian networks. Physica A—Statistical Mechanics and Its Applications, 389(22), 5298–5307.

    Article  Google Scholar 

  • Xie, J. H., Song, X. M., Sirbu, M., & Wang, Q. (1997). Kalman filter estimation of new product diffusion models. Journal of Marketing Research, 34(3), 378–393.

    Article  Google Scholar 

  • Zhang, G., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501–514.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, China

    Zhaoxia Guo PhD

Authors
  1. Zhaoxia Guo PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaoxia Guo PhD .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Guo, Z. (2016). An Extreme Learning Machine-Based Intelligent Decision-Making Model for Multivariate 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_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-52681-1_11

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-52679-8

  • Online ISBN: 978-3-662-52681-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation