References
- Adhikari, R., Agrawal, R. (2014). A combination of artificial neural network and random walk models for financial time series forecasting. Neural Computing and Applications 24:1441–1449.
- Aladag, C. H., Egrioglu, E., Kadilar, C. (2012). Improvement in forecasting accuracy using the hybrid model of arfima and feed forward neural network. American Journal of Intelligent Systems 2:12–17.
- Aksu, I. O., Coban, R. (2013). Identification of disk drive systems using the multifeedback-layer neural network and the particle swarm optimization algorithm. Technological advances in electrical, electronics and computer engineering (TAEECE). 2013 International Conference on, IEEE, pp. 230–23.
- Baghebo, M., Atima, T. O. (2013). The impact of petroleum on economic growth in Nigeria. Global Business and Economics Research Journal 2:102–115.
- Becker, A. B. D. (2011). Decomposition methods for large scale stochastic and robust optimization problems. Ph.D. thesis, Massachusetts Institute of Technology.
- Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (2008). Time series analysis: Forecasting and control. Hoboken, New Jersey: Wiley.
- Chai, T., Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding rmse in the literature. Geoscientific Model Development 7:1247–1250.
- Chatfield, C. (1996). Model uncertainty and forecast accuracy. Journal of Forecasting 15:495–508.
- Contreras-Reyes, J. E., Palma, W. (2013). Statistical analysis of autoregressive fractionally integrated moving average models in R. Computational Statistics 28:2309–2331.
- Dickey, D. A., Fuller, W. A. (1979). Distribution of the estimates for autoregressive time series with a unit root. Journal of American Statistical Association 74:427–431.
- Giacomini, R., White, H. (2006). Tests of conditional predictive ability. Econometrica 74, 1545–1578.
- Granger, C. W., Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of time series analysis 1:15–29.
- Jin, H., Kim, S. (2015). Performance evaluations of diagnostic prediction with neural networks with data filters in different types. Neural Networks 7(1):61–70.
- Latifoğlu, L., Kişi, Ö., Latifoğlu, F. (2015). Importance of hybrid models for forecasting of hydrological variable. Neural Computing and Applications 26(7):1669–1680.
- Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics 2:164–168.
- Liu, Q., Guo, Z., Wang, J. (2012). A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks 26:99–109.
- Lu, C.-J. (2013). Hybridizing nonlinear independent component analysis and support vector regression with particle swarm optimization for stock index forecasting. Neural Computing and Applications 23:2417–2427.
- Marquardt, D. (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 11(2):431–441. doi:10.1137/0111030
- Paliwal, M., Kumar, U. A. (2009). Neural networks and statistical techniques: A review of applications. Expert systems with applications 36:2–17.
- Raftery, A. E., Madigan, D., Hoeting, J. A. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association 92:179–191.
- Saigal, S., Mehrotra, D. (2012). Performance comparison of time series data using predictive data mining techniques. Advances in Information Mining 4:57–66.
- Sudheer, C., Maheswaran, R., Panigrahi, B. K., Mathur, S. (2014). A hybrid svm-pso model for forecasting monthly streamflow. Neural Computing and Applications 24:1381–1389.
- Suen, J.-P., Eheart, J. W. (2003). Evaluation of neural networks for modeling nitrate concentrations in rivers. Journal of Water Resources Planning and Management 129:505–510.
- Willmott, C. J., Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research 30(1):79–82.
- Willmott, C. J., Matsuura, K., Robeson, S. M. (2009). Ambiguities inherent in sum-of-squares-based error statistics. Atmospheric Environment 43(3):749–752.
- Young, C.-C., W.-C. Liu, Chung, C.-E. (2015). Genetic algorithm and fuzzy neural networks combined with the hydrological modeling system for forecasting watershed runoff discharge. Neural Computing and Applications 26(7):1–13.
- Zhang, Z., Tang, Z., Vairappan, C. (2007). A novel learning method for elman neural network using local search. Neural Information Processing–Letters and Reviews 11:181–188.
- Zheng, F., Zhong, S. (2011). Time series forecasting using a hybrid rbf neural network and ar model based on binomial smoothing, World academy of science and technology 75:419–423.