Estimating the impact of shocks with artificial neural networks

  • Authors:

  • Konstantinos Nikolopoulos;Nikolaos Bougioukos;Konstantinos Giannelos;Vassilios Assimakopoulos

  • Affiliations:

  • Decision Sciences and Operations Management, Manchester Business School, Manchester, United Kingdom;Forecasting Systems Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Forecasting Systems Unit, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;Special Secretary for Digital Planning, Ministry of Economy and Finance, Athens, Greece

  • Venue:
  • ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
  • Year:
  • 2007

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Abstract

Quantitative models are very successful forr extrapolating the basic trend-cycle component of time series. On the contrary time series models failed to handle adequately shocks or irregular events, that is non-periodic events such as oil crises, promotions, strikes, announcements, legislation etc. Forecasters usually prefer to use their own judgment in such problems. However their efficiency in such tasks is in doubt too and as a result the need of decision support tools in this procedure seem to be quite important. Forecasting with neural networks has been very popular across the Academia in the last decade. Estimating the impact of irregular events has been one of the most successful application areas. This study examines the relative performance of Artificial Neural Networks versus Multiple Linear Regression for estimating the impact of expected irregular future events.