IDEAS home Printed from https://ideas.repec.org/a/ris/buecrj/0672.html
   My bibliography  Save this article

Forecasting Drinking Water Sales Values with Artificial Neural Networks: A Comparative Analysis with ARIMA and Winters’ Methods

Author

Listed:
  • Tüzüntürk, Selim

    (Bursa Uludag University)

Abstract

This study aimed to forecast drinking water sales accurately for a water company dealer using the artificial neural networks method. The data used in this study is the total monthly sales number of dispenser-size water bottles of a water company's dealer in Bursa. The data consists of 85 months, from May 2017 to May 2024. In this context, an artificial neural network model was developed, and the estimations' performance was quite good. The histogram of estimation errors and normality tests showed a normal distribution. The findings show that the network can generalize. Besides this, visualizing the actual and estimated values showed that they follow the same patterns. As a result, it was concluded that monthly sales can be forecasted with the model developed using the threshold values and weights obtained from the trained network. Long-term forecasts were made and interpreted for the water company dealer using the developed model. Finally, the proposed artificial neural network was validated by comparing it with the average absolute percentage error values of alternative models, seasonal autoregressive integrated moving average, and seasonal exponential smoothing models.

Suggested Citation

  • Tüzüntürk, Selim, 2024. "Forecasting Drinking Water Sales Values with Artificial Neural Networks: A Comparative Analysis with ARIMA and Winters’ Methods," Business and Economics Research Journal, Uludag University, Faculty of Economics and Administrative Sciences, vol. 15(4), pages 371-388, October.
    • Handle: RePEc:ris:buecrj:0672
    as

    Download full text from publisher

    File URL: https://www.berjournal.com/forecasting-drinking-water-sales-values-with-artificial-neural-networks-a-comparative-analysis-with-arima-and-winters-methods
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Forecasting; Sales Forecast; Drinking Water; Artificial Neural Networks; ARIMA; Winters’ Method;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:buecrj:0672. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Adem Anbar (email available below). General contact details of provider: https://edirc.repec.org/data/iiulutr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.