How to cite this paper
Narimani, R & Narimani, A. (2012). A New Hybrid Model for Improvement of ARIMA by DEA.Decision Science Letters , 1(2), 59-68.
Refrences
Asadi, S., Tavakoli, A., & Hejazi, S. R. (2012). A new hybrid for improvement of auto-regressive integrated moving average models applying particle swarm optimization. Expert Systems with Applications, 39, 5332-5337.
Avkiran, (2006). Developing foreign bank efficiency models for DEA grounded in finance theory. Socio-Economic Planning Sciences, 40, 275–296
Box, P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco, CA: Holden-day Inc.
Burnham, K., & Anderson, D. (2004). Multimodel inference: understanding {AIC} and {BIC} in model selection.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica 49, 1057-1072.
Khashei, M., Bijari, M., & RaissiArdali, G. A. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers & Industrial Engineering, 63, 37-45.
Minerva, T., & Poli, I. (2001). Building ARMA models with genetic algorithms. Applications of Evolutionary Computing. In E. Boers (Ed.), 2037, 335-342, Springer Berlin / Heidelberg.
Salcedo-Sanz, S., Prado-Cumplido, M., Pérez-Cruz, F., & Bouso?o-Calz?n, C. (2002). Feature Selection via Genetic Optimization. Artificial Neural Networks — ICANN 2002. In J. Dorronsoro (Ed.), 2415, 44-144, Springer Berlin / Heidelberg.
Sarkis, J. (2000). An analysis of the operational efficiency of major airports in the United States. Journal of Operations Management, 18, 335-351.
Spanos, A. (2010). Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification. Journal of Econometrics, 158, 204-220.
Shibata, R. (1976). Selection of the order of an autoregressive model by Akaike & apos; s information criterion. Biometrika, 63, 117-126.
Sun, S., & Lu, W. (2005). Evaluating the performance of the Taiwanese hotel industry using a weight slacks-based measure. Asia–Pacific Journal of Operational Research, 22, 487–512.
Walker, G. (1931). On periodicity in series of related terms. Monthly Weather Review, 59, 277-278.
Valenzuela, O., Rojas, I., Rojas, F., Pomares, H., Herrera, L. J., Guillen, A., Marquez, L., & Pasadas, M. (2008). Hybridization of intelligent techniques and ARIMA models for time series prediction. Fuzzy Sets and Systems, 159, 821-845.
Wang, H., & Zhao, W. (2009). ARIMA model estimated by particle swarm optimization algorithm for consumer price index forecasting. Artificial Intelligence and Computational Intelligence. In H. Deng, L. Wang, F. Wang & J. Lei (Eds.), 5855, 48-58, Springer Berlin / Heidelberg.
Yule, U. (1927). On a method for investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London, 226, 267 - 298.
Zhao, Z., Zhang, Y., & Liao, H. (2008). Design of ensemble neural network using the Akaike information criterion. Engineering Applications of Artificial Intelligence, 21, 1182-1188.
Avkiran, (2006). Developing foreign bank efficiency models for DEA grounded in finance theory. Socio-Economic Planning Sciences, 40, 275–296
Box, P., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco, CA: Holden-day Inc.
Burnham, K., & Anderson, D. (2004). Multimodel inference: understanding {AIC} and {BIC} in model selection.
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica 49, 1057-1072.
Khashei, M., Bijari, M., & RaissiArdali, G. A. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers & Industrial Engineering, 63, 37-45.
Minerva, T., & Poli, I. (2001). Building ARMA models with genetic algorithms. Applications of Evolutionary Computing. In E. Boers (Ed.), 2037, 335-342, Springer Berlin / Heidelberg.
Salcedo-Sanz, S., Prado-Cumplido, M., Pérez-Cruz, F., & Bouso?o-Calz?n, C. (2002). Feature Selection via Genetic Optimization. Artificial Neural Networks — ICANN 2002. In J. Dorronsoro (Ed.), 2415, 44-144, Springer Berlin / Heidelberg.
Sarkis, J. (2000). An analysis of the operational efficiency of major airports in the United States. Journal of Operations Management, 18, 335-351.
Spanos, A. (2010). Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification. Journal of Econometrics, 158, 204-220.
Shibata, R. (1976). Selection of the order of an autoregressive model by Akaike & apos; s information criterion. Biometrika, 63, 117-126.
Sun, S., & Lu, W. (2005). Evaluating the performance of the Taiwanese hotel industry using a weight slacks-based measure. Asia–Pacific Journal of Operational Research, 22, 487–512.
Walker, G. (1931). On periodicity in series of related terms. Monthly Weather Review, 59, 277-278.
Valenzuela, O., Rojas, I., Rojas, F., Pomares, H., Herrera, L. J., Guillen, A., Marquez, L., & Pasadas, M. (2008). Hybridization of intelligent techniques and ARIMA models for time series prediction. Fuzzy Sets and Systems, 159, 821-845.
Wang, H., & Zhao, W. (2009). ARIMA model estimated by particle swarm optimization algorithm for consumer price index forecasting. Artificial Intelligence and Computational Intelligence. In H. Deng, L. Wang, F. Wang & J. Lei (Eds.), 5855, 48-58, Springer Berlin / Heidelberg.
Yule, U. (1927). On a method for investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London, 226, 267 - 298.
Zhao, Z., Zhang, Y., & Liao, H. (2008). Design of ensemble neural network using the Akaike information criterion. Engineering Applications of Artificial Intelligence, 21, 1182-1188.