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

  • Nikolopoulos K
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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. © 2010 Taylor & Francis.

Author-supplied keywords

  • artificial intelligence; artificial neural network

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  • K Nikolopoulos

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