Abstract
Time series often exhibit periodical patterns that can be analysed by conventional statistical techniques. These techniques rely upon an appropriate choice of model parameters that are often difficult to determine. Whilst neural networks also require an appropriate parameter configuration, they offer a way in which non-linear patterns may be modelled. However, evidence from a limited number of experiments has been used to argue that periodical patterns cannot be modelled using such networks. In this paper, we present a method to overcome the perceived limitations of this approach by determining the configuration parameters of a time delayed neural network from the seasonal data it is being used to model. Our method uses a fast Fourier transform to calculate the number of input tapped delays, with results demonstrating improved performance as compared to that of other linear and hybrid seasonal modelling techniques.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chatfield, C.: The Analysis of Time Series, 6th edn. Texts in Statistical Science. Chapman and Hall, Boca Raton (2004)
Rementeria, S., Olabe, X.B.: Predicting sunspots with a self-configuring neural system. In: Proceedings of the 8th Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2000 (2000)
Box, G., Jenkins, G.: Time Series Analysis: forecasting and control. Texts in Statistical Science. Holden Day (1970)
Faraway, J., Chatfield, C.: Time series forecasting with neural networks: A comparative study using the airline data. Applied Statistics 47, 231–250 (1998)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Rao, T.S., Sabr, M.M.: An introduction to bispectral analysis and bilinear time series models. Lecture Notes in Statistics, 24 (1984)
Wan, E.: Time series prediction by using a connectionist network with internal delay lines. In: Weigend, A., Gershenfeld, N. (eds.) Time Series Prediction: Forecasting the Future and Understanding the Past. SFI Studies in the Sciences of Complexity, pp. 1883–1888. Addison-Wesley, Reading (1994)
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., Lang, K.J.: Phoneme recognition using time-delay neural networks. IEEE Transactions on Acoustics, Speech and Signal Processing 37, 328–339 (1989)
Cottrell, M., Girard, B., Girard, Y., Mangeas, M., Muller, C.: Neural modeling for time series:a statistical stepwise method for weight elimination. IEEE Transactions on Neural Networks 6, 1355–1364 (1995)
Taskaya-Temizel, T., Casey, M.C., Ahmad, K.: Pre-processing inputs for optimally-configured time-delay neural networks. IEE Electronic Letters 41, 198–200 (2005)
Taskaya-Temizel, T., Ahmad, K.: Are ARIMA neural network hybrids better than single models? In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2005), Montréal, Canada (2005) (to appear)
Zhang, G.P., Qi, M.: Neural network forecasting for seasonal and trend time series. European Journal of Operational Research 160, 501–514 (2005)
Tseng, F.M., Yu, H.C., Tzeng, G.H.: Combining neural network model with seasonal time series ARIMA model. Technological Forecasting and Social Change 69, 71–87 (2002)
Box, G.E.P., Cox, D.R.: An analysis of transformations. JRSS B 26, 211–246 (1996)
Virili, F., Freisleben, B.: Nonstationarity and data preprocessing for neural network predictions of an economic time series. In: Proceedings of International Joint Conference on Neural Networks (IJCNN 2000), Como, Italy, pp. 5129–5136 (2000)
Nelson, M., Hill, T., Remus, W., O’Connor, M.: Time series forecasting using neural networks: Should the data be deseasonalized first? Journal of Forecasting 18, 359–367 (1999)
Weigend, A.S., Gershenfeld, N.-A.: Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading (1993)
Federal Reserve Time Serial Data (2005) Last accessed, (January 2005), http://www.federalreserve.gov/releases/G17/table1_2.htm
U.S. Census Bureau Monthly Trade and Food Services Data Sets (2005) Last accessed (January 2005), http://www.census.gov/mrts/www/mrts.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Taskaya-Temizel, T., Casey, M.C. (2005). Configuration of Neural Networks for the Analysis of Seasonal Time Series. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_32
Download citation
DOI: https://doi.org/10.1007/11551188_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
eBook Packages: Computer ScienceComputer Science (R0)