IDEAS home Printed from https://ideas.repec.org/a/jfr/ijba11/v9y2018i5p11-20.html
   My bibliography  Save this article

Forecasting the Daily Transaction Data Utilizing a Day of the Week Index in the Case of Web Site

Author

Listed:
  • Kazuhiro Takeyasu
  • Tomohiro Watanabe

Abstract

The daily web site access data¡¯s forecasting is an important factor for the web master in order to improve the quality of the site, and to increase the sales from the site. In this paper, the authors propose a new method to improve forecasting accuracy and confirm them by the Web transaction data (Nail consulting site). Focusing that the equation of exponential smoothing method (ESM) is equivalent to (1,1) order ARMA model equation, a new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Generally, smoothing constant is selected arbitrarily. But in this paper, we utilize above stated theoretical solution. Firstly, the authors make estimation of ARMA model parameter and then estimate smoothing constants. Thus theoretical solution is derived in a simple way and it may be utilized in various fields. Combining the trend removing method with this method, we aim to improve forecasting accuracy. Furthermore, ¡°a day of the week index¡± is newly introduced for the daily data and we have obtained good result. The effectiveness of this method should be examined in various cases.

Suggested Citation

  • Kazuhiro Takeyasu & Tomohiro Watanabe, 2018. "Forecasting the Daily Transaction Data Utilizing a Day of the Week Index in the Case of Web Site," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 9(5), pages 11-20, September.
    • Handle: RePEc:jfr:ijba11:v:9:y:2018:i:5:p:11-20
    DOI: 10.5430/ijba.v9n4p11
    as

    Download full text from publisher

    File URL: http://www.sciedu.ca/journal/index.php/ijba/article/view/13999/8643
    Download Restriction: no
    File URL: http://www.sciedu.ca/journal/index.php/ijba/article/view/13999
    Download Restriction: no
    File URL: https://libkey.io/10.5430/ijba.v9n4p11?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bilgili, Faik, 2002. "VAR, ARIMA, Üstsel Düzleme, Karma ve İlave-Faktör Yöntemlerinin Özel Tüketim Harcamalarına ait Ex Post Öngörü Başarılarının Karşılaştırılması [A Comparison of Ex-Post Forecast Accuracies for VAR, A," MPRA Paper 75536, University Library of Munich, Germany, revised 2002.
    2. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    3. Aye, Goodness C. & Balcilar, Mehmet & Gupta, Rangan & Majumdar, Anandamayee, 2015. "Forecasting aggregate retail sales: The case of South Africa," International Journal of Production Economics, Elsevier, vol. 160(C), pages 66-79.
    4. Srinivas Bollapragada & Salil Gupta & Brett Hurwitz & Paul Miles & Rajesh Tyagi, 2008. "NBC-Universal Uses a Novel Qualitative Forecasting Technique to Predict Advertising Demand," Interfaces, INFORMS, vol. 38(2), pages 103-111, April.
    5. Zhenni Ding & Huayou Chen & Ligang Zhou, 2023. "Using shapely values to define subgroups of forecasts for combining," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 905-923, July.
    6. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    7. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    8. Julia A. Minson & Jennifer S. Mueller & Richard P. Larrick, 2018. "The Contingent Wisdom of Dyads: When Discussion Enhances vs. Undermines the Accuracy of Collaborative Judgments," Management Science, INFORMS, vol. 64(9), pages 4177-4192, September.
    9. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    10. Park, Timothy A. & Gubanova, Tatiana & Lohr, Luanne & Escalante, Cesar L., 2005. "Forecasting Organic Food Prices: Testing and Evaluating Conditional Predictive Ability," 2005 Annual meeting, July 24-27, Providence, RI 19412, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2021. "Distributional regression for demand forecasting in e-grocery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 831-842.
    12. Hensher, D. A. & Louviere, J. J. & Hansen, D. E., 2000. "The use of mixtures of market and experimental choice data in establishing guideline weights for evaluating competitive bids in a transport organisation," Transport Policy, Elsevier, vol. 7(4), pages 279-286, October.
    13. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    14. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.
    15. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    16. WenJie Wang & Qi Xu & Dandan Fan, 2018. "Stein-Rule Combination Forecasting on RFID Based Supply Chain," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(02), pages 1-13, April.
    17. Evlakhova, Yu. & Alifanova, E. & Tregubova, A., 2021. "Banks behavior patterns as a response to the population financial activity in the macroeconomic shocks in Russia," Journal of the New Economic Association, New Economic Association, vol. 50(2), pages 74-95.
    18. Lisheng He & Pantelis P. Analytis & Sudeep Bhatia, 2022. "The Wisdom of Model Crowds," Management Science, INFORMS, vol. 68(5), pages 3635-3659, May.
    19. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    20. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.

    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:jfr:ijba11:v:9:y:2018:i:5:p:11-20. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Jenny Zhang (email available below). General contact details of provider: http://ijba.sciedupress.com .

    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.