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International Journal of Forecasting 27 (2011) 672688 www.elsevier.com/locate/ijforecast

Summary: International Journal of Forecasting 27 (2011) 672688
Forecast combinations of computational intelligence and linear
models for the NN5 time series forecasting competition
Robert R. Andrawisa, Amir F. Atiyaa,, Hisham El-Shishinyb
a Department of Computer Engineering, Cairo University, Giza, Egypt
b IBM Center for Advanced Studies in Cairo, IBM Cairo Technology Development Center, Giza, Egypt
Available online 8 January 2011
In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the
forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model
is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature.
In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts
that are known in the forecasting literature to lead to superior performance. For example, we considered various previous
comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for
possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian
process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect,
decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.
c 2010 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords: NN5 competition; Time series forecasting; Neural network forecasting; Gaussian process forecasting; Forecast combination;


Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology


Collections: Computer Technologies and Information Sciences