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

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  • 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
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    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.

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