References
- 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. https://doi.org/10.1002/(SICI)1099-131X(1998090)17:5/6<481::AID-FOR709>3.0.CO;2-Q
- 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. https://doi.org/10.1016/j.ins.2007.05.024
- Berg, D., "Bankruptcy prediction by generalized additive models", Applied Stochastic Models in Business and Industry, Vol.23(2007), 129-143.
- 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. https://doi.org/10.1016/j.ejor.2005.02.012
- Box, G. E. P. and G. M. Jenkins, Time Series Analysis : Forecasting and Control, San Francisco, CA : Holden-Day, 1976.
- 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. https://doi.org/10.1016/j.eswa.2006.07.005
- 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.
- 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. https://doi.org/10.1093/aje/kwf062
- 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. https://doi.org/10.1016/j.ejor.2005.06.057
- 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. https://doi.org/10.1111/0824-7935.00090
- Hastie, T. and Tibshirani, R., "Generalized additive models", Statistical Science, Vol.1(1986), 297-310. https://doi.org/10.1214/ss/1177013604
- Hyndman, R. J., Forecasting functions for time series and linear models, R package version (http://cran.r-project.org/web/packages/forec ast/), (2012).
- Ittig, P. T., "A seasonal index for business", Decision Sciences, Vol.28(1997), 335-355. https://doi.org/10.1111/j.1540-5915.1997.tb01314.x
- Montgomery, D. C., L. A. Johnson, and J. S. Gardiner, Time Series Analysis. McGraw- Hill, New York, 1990.
- 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. https://doi.org/10.1016/0304-3932(82)90012-5
- 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. https://doi.org/10.1002/(SICI)1099-131X(199909)18:5<359::AID-FOR746>3.0.CO;2-P
- 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. https://doi.org/10.1002/(SICI)1099-128X(199701)11:1<13::AID-CEM430>3.0.CO;2-K
- 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. https://doi.org/10.1016/S0040-1625(00)00113-X
- Wood, S. N., Generalized additive models : An introduction with R. Florida : Chapman and Hall/CRC, 2006.
- Zhang, G. P., "Time series forecasting using a hybrid ARIMA and neural network model", Neurocomputing, Vol.50(2003), 159-175. https://doi.org/10.1016/S0925-2312(01)00702-0