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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aiken, M.: Using a neural network to forecast inflation. Industrial Management and Data Systems 99, 296–301 (1999)
Armstrong, J.S.: Principles of Forecasting: A handbook for researchers and practitioners. Kluwer Academic Publishers, Dordrecht (2001)
Assimakopoulos, V., Nikolopoulos, K.: The theta model: a decomposition approach to forecasting. International Journal of Forecasting 16, 521–530 (2000)
Balkin, S., Ord, K.: Automatic Neural Net Modelling for univariate time series. International Journal of Forecasting 16, 509–515 (2000)
Chu, C., Zhang, G.P.: A comparative study of linear and nonlinear models for aggregate retail sales forecasting. International Journal of Production Economics 86, 217–231 (2003)
Fildes, R., Hibon, M., Makridakis, S., Meade, N.: Generalising about univariate forecasting methods: further empirical evidence. International Journal of Forecasting 14, 339–358 (1998)
Flores, B.E., Olson, D.L., Wolfe, C.: Judgmental adjustment of forecasts: A comparison of methods. International Journal of Forecasting 7, 421–433 (1992)
Goodwin, P.: Improving the voluntary integration of statistical forecasts and judgment. International Journal of Forecasting 16, 85–99 (2000)
Goodwin, P.: Integrating management judgment and statistical methods to improve short-term forecasts. Omega 30, 127–135 (2002)
Haykin, S.: Neural Networks: A Comprehensive Foundation (International Edition), Pearson US Imports & PHIPEs. New York (1998)
Heravi, S., Osborn, D.R., Birchenhall, C.R.: Linear versus neural network forecasts for European industrial production series. International Journal of Forecasting 20, 435–446 (2004)
Kim, K.-J.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)
Lawrence, M., O’Connor, M.: Judgment or models: The importance of task differences. Omega 24, 245–254 (1996)
Lee, J.K., Yum, C.S.: Judgmental adjustment in time series forecasting using neural networks. Decision Support Systems 22, 135–154 (1998)
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., Winkler, R.: The forecasting accuracy of major time series methods. John Wiley & Sons, New York (1984)
Makridakis, S., Wheelwright, S., Hyndman, R.: Forecasting, Methods and Applications, 3rd edn., Wiley, New York (1998)
Makridakis, S., Hibon, M.: The M3-Competition: Results, conclusions and implications. International Journal of Forecasting 16, 451–476 (2000)
Nikolopoulos, K., Assimakopoulos, V.: Theta Intelligent Forecasting Information System. Industrial Management and Data Systems 103, 711–726 (2003)
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks. The state of the art. International Journal of Forecasting 14, 35–62 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nikolopoulos, K., Bougioukos, N., Giannelos, K., Assimakopoulos, V. (2007). Estimating the Impact of Shocks with Artificial Neural Networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_49
Download citation
DOI: https://doi.org/10.1007/978-3-540-74695-9_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74693-5
Online ISBN: 978-3-540-74695-9
eBook Packages: Computer ScienceComputer Science (R0)