IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v42y2010i8p947-955.html
   My bibliography  Save this article

Forecasting with quantitative methods: the impact of special events in time series

Author

Listed:
  • Konstantinos Nikolopoulos

Abstract

Quantitative methods are very successful in producing baseline forecasts of time series; however, these models forecast neither the timing nor the impact of special events such as promotions or strikes. In most of the cases, the timing of such events is not known so they are usually referred as shocks (economics) or special events (forecasting). Sometimes the timing of such events is known a priori (i.e. a future promotion); but even then the impact of the forthcoming event is hard to estimate. Forecasters prefer to use their own judgement for adjusting for forthcoming special events, but human efficiency in such tasks has been found to be deficient. This study after examining the relative performance of Artificial Neural Networks (ANNs), Multiple Linear Regression (MLR) and Nearest Neighbour (NN) approaches proposes an expert method, which combines the strengths of regression and artificial intelligence.

Suggested Citation

  • Konstantinos Nikolopoulos, 2010. "Forecasting with quantitative methods: the impact of special events in time series," Applied Economics, Taylor & Francis Journals, vol. 42(8), pages 947-955.
    • Handle: RePEc:taf:applec:v:42:y:2010:i:8:p:947-955
    DOI: 10.1080/00036840701721042
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/00036840701721042
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036840701721042?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fildes, Robert & Hibon, Michele & Makridakis, Spyros & Meade, Nigel, 1998. "Generalising about univariate forecasting methods: further empirical evidence," International Journal of Forecasting, Elsevier, vol. 14(3), pages 339-358, September.
    2. Green, Kesten C., 2002. "Forecasting decisions in conflict situations: a comparison of game theory, role-playing, and unaided judgement," International Journal of Forecasting, Elsevier, vol. 18(3), pages 321-344.
    3. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
    4. Stewart, Thomas R., 1999. "Commentary on "The Delphi technique as a forecasting tool: Issues and analysis" by Rowe and Wright," International Journal of Forecasting, Elsevier, vol. 15(4), pages 380-381, October.
    5. Sanders, Nada R. & Manrodt, Karl B., 2003. "The efficacy of using judgmental versus quantitative forecasting methods in practice," Omega, Elsevier, vol. 31(6), pages 511-522, December.
    6. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
    7. D. E. Allen & H. M. Salim, 2005. "Forecasting profitability and earnings: a study of the UK market (1982-2000)," Applied Economics, Taylor & Francis Journals, vol. 37(17), pages 2009-2018.
    8. Ayton, Peter & Ferrell, William R. & Stewart, Thomas R., 1999. "Commentaries on "The Delphi technique as a forecasting tool: issues and analysis" by Rowe and Wright," International Journal of Forecasting, Elsevier, vol. 15(4), pages 377-379, October.
    9. Yao, Jingtao & Li, Yili & Tan, Chew Lim, 2000. "Option price forecasting using neural networks," Omega, Elsevier, vol. 28(4), pages 455-466, August.
    10. Jaesun Noh & Tae-Hwan Kim, 2006. "Forecasting volatility of futures market: the S&P 500 and FTSE 100 futures using high frequency returns and implied volatility," Applied Economics, Taylor & Francis Journals, vol. 38(4), pages 395-413.
    11. Flores, Benito E. & Olson, David L. & Wolfe, Christopher, 1992. "Judgmental adjustment of forecasts: A comparison of methods," International Journal of Forecasting, Elsevier, vol. 7(4), pages 421-433, March.
    12. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    13. K. Maris & K. Nikolopoulos & K. Giannelos & V. Assimakopoulos, 2007. "Options trading driven by volatility directional accuracy," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 253-260.
    14. Fildes, Robert & Nikolopoulos, Konstantinos, 2006. "Spyros Makridakis: An interview with the International Journal of Forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 625-636.
    15. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    16. Goodwin, Paul, 2000. "Improving the voluntary integration of statistical forecasts and judgment," International Journal of Forecasting, Elsevier, vol. 16(1), pages 85-99.
    17. Laroche, Michel & Pons, Frank & Zgolli, Nadia & Cervellon, Marie-Cecile & Kim, Chankon, 2003. "A model of consumer response to two retail sales promotion techniques," Journal of Business Research, Elsevier, vol. 56(7), pages 513-522, July.
    18. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    19. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    20. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos, 2005. "A technical analysis approach to tourism demand forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 327-333.
    21. George Halkos & Ilias Kevork, 2006. "Forecasting the stationary AR(1) with an almost unit root," Applied Economics Letters, Taylor & Francis Journals, vol. 13(12), pages 789-793.
    22. Perry Sadorsky, 2005. "Stochastic volatility forecasting and risk management," Applied Financial Economics, Taylor & Francis Journals, vol. 15(2), pages 121-135.
    23. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
    24. K. Maris & G. Pantou & K. Nikolopoulos & E. PagourtzI & V. Assimakopoulos, 2004. "A study of financial volatility forecasting techniques in the FTSE/ASE 20 index," Applied Economics Letters, Taylor & Francis Journals, vol. 11(7), pages 453-457.
    25. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
    26. Goodwin, Paul, 2002. "Integrating management judgment and statistical methods to improve short-term forecasts," Omega, Elsevier, vol. 30(2), pages 127-135, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    3. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    4. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.

    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. Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.
    2. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    3. Eksoz, Can & Mansouri, S. Afshin & Bourlakis, Michael, 2014. "Collaborative forecasting in the food supply chain: A conceptual framework," International Journal of Production Economics, Elsevier, vol. 158(C), pages 120-135.
    4. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    5. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    6. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    7. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    8. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    9. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    10. Lee, Wing Yee & Goodwin, Paul & Fildes, Robert & Nikolopoulos, Konstantinos & Lawrence, Michael, 2007. "Providing support for the use of analogies in demand forecasting tasks," International Journal of Forecasting, Elsevier, vol. 23(3), pages 377-390.
    11. Alvarado-Valencia, Jorge & Barrero, Lope H. & Önkal, Dilek & Dennerlein, Jack T., 2017. "Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(1), pages 298-313.
    12. Leitner, Johannes & Leopold-Wildburger, Ulrike, 2011. "Experiments on forecasting behavior with several sources of information - A review of the literature," European Journal of Operational Research, Elsevier, vol. 213(3), pages 459-469, September.
    13. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    14. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).
    15. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    16. Baecke, Philippe & De Baets, Shari & Vanderheyden, Karlien, 2017. "Investigating the added value of integrating human judgement into statistical demand forecasting systems," International Journal of Production Economics, Elsevier, vol. 191(C), pages 85-96.
    17. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    18. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    19. Sroginis, Anna & Fildes, Robert & Kourentzes, Nikolaos, 2023. "Use of contextual and model-based information in adjusting promotional forecasts," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1177-1191.
    20. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.

    More about this item

    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:taf:applec:v:42:y:2010:i:8:p:947-955. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.