Browse > Article
http://dx.doi.org/10.13088/jiis.2013.19.1.001

Comparison Studies of Hybrid and Non-hybrid Forecasting Models for Seasonal and Trend Time Series Data  

Jeong, Chulwoo (Korea Institute for Defense Analyses)
Kim, Myung Suk (Global Service Management Department, Sogang Business School)
Publication Information
Journal of Intelligence and Information Systems / v.19, no.1, 2013 , pp. 1-17 More about this Journal
Abstract
In this article, several types of hybrid forecasting models are suggested. In particular, hybrid models using the generalized additive model (GAM) are newly suggested as an alternative to those using neural networks (NN). The prediction performances of various hybrid and non-hybrid models are evaluated using simulated time series data. Five different types of seasonal time series data related to an additive or multiplicative trend are generated over different levels of noise, and applied to the forecasting evaluation. For the simulated data with only seasonality, the autoregressive (AR) model and the hybrid AR-AR model performed equivalently very well. On the other hand, if the time series data employed a trend, the SARIMA model and some hybrid SARIMA models equivalently outperformed the others. In the comparison of GAMs and NNs, regarding the seasonal additive trend data, the SARIMA-GAM evenly performed well across the full range of noise variation, whereas the SARIMA-NN showed good performance only when the noise level was trivial.
Keywords
Forecasting; Generalized Additive Models; Seasonal ARIMA; Neural Networks; Hybrid Models;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Adya, M. and F. Collopy, "How effective are neural networks at forecasting and prediction? A review and evaluation", Journal of Forecasting, Vol.17(1998), 481-495.   DOI
2 Barbounis, T. G. and J. B. Teocharis, "Locally recurrent neural networks for wind speed prediction using spatial correlation", Information Science, Vol.177(2007), 5775-5797.   DOI   ScienceOn
3 Berg, D., "Bankruptcy prediction by generalized additive models", Applied Stochastic Models in Business and Industry, Vol.23(2007), 129-143.
4 Box, G. E. P. and G. M. Jenkins, Time Series Analysis : Forecasting and Control, San Francisco, CA : Holden-Day, 1976.
5 Celik, A. E. and Y. Karatepe, "Evaluating and forecasting banking crises through neural network models : an application for Turkish banking sector", Expert Systems with Applications, Vol.33(2007), 809-815.   DOI   ScienceOn
6 Di Narzo, A. F., J. L. Aznarte, and M. Stigler, Nonlinear time series models with regime switching, R package version (http://cran.us.rproject. org/web/packages/tsDyn/), 2012.
7 Freitas, P. S. A. and A. J. L. Rodrigues, "Model combination in neural-based forecasting", European Journal of Operational Research, Vol. 173(2006), 801-814.   DOI   ScienceOn
8 Hansen, J. V., J. B. McDonald, and R. D. Nelson, "Time series prediction with genetic-algorithm designed neural networks : an empirical comparison with modern statistical models", Computational Intelligence, Vol.15(1999), 171-184.   DOI
9 Hastie, T. and Tibshirani, R., "Generalized additive models", Statistical Science, Vol.1(1986), 297-310.   DOI   ScienceOn
10 Hyndman, R. J., Forecasting functions for time series and linear models, R package version (http://cran.r-project.org/web/packages/forec ast/), (2012).
11 Ittig, P. T., "A seasonal index for business", Decision Sciences, Vol.28(1997), 335-355.   DOI   ScienceOn
12 Montgomery, D. C., L. A. Johnson, and J. S. Gardiner, Time Series Analysis. McGraw- Hill, New York, 1990.
13 Nelson, C. R. and C. I. Plosser, "Trends and random walks in macroeconomic time series: Some evidence and implications", Journal of Monetary Economics, Vol.10(1982), 139-162.   DOI   ScienceOn
14 Nelson, M., T. Hill, W. Renus, and M. O'Connor, "Time series forecasting using neural networks : should the data be deseasonalized first?", Journal of Forecasting, Vol.18(1999), 359-367.   DOI
15 Prada-Sanchez, J. M. and M. Febrero-Bande, "Parametric, non-parametric and mixed approaches to prediction of sparsely distributed pollution incidents : a case study", Journal of Chemometrics, Vol.11(1997), 13-32.   DOI
16 Wood, S. N., Generalized additive models : An introduction with R. Florida : Chapman and Hall/CRC, 2006.
17 Zhang, G. P., "Time series forecasting using a hybrid ARIMA and neural network model", Neurocomputing, Vol.50(2003), 159-175.   DOI   ScienceOn
18 Tseng, F. M., H. C. Yu, and G. H. Tzeng, "Combining neural network model with seasonal time series ARIMA model", Technological Forecasting and Social Change, Vol.69(2002), 71-87.   DOI   ScienceOn
19 Bodyanskiy, Y. and S. Popov, "Neural network approach to forecasting of quasiperiodic financial time series", European Journal of Operational Research, Vol.175(2006), 1357-1366.   DOI   ScienceOn
20 Dominici, F., A. McDermott, S. L. Zeger, and J. M. Samet, "On the use of generalized additive models in time-series studies of air pollution and health", American Journal of Epidemiology, Vol.156(2002), 193-203.   DOI   ScienceOn