A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

Gonzalez-Romera, 2006, Monthly electric energy demand forecasting based on trend extraction, IEEE Trans. Power Syst., 21, 1946, 10.1109/TPWRS.2006.883666

Contreras, 2003, ARIMA models to predict next-day electricity prices, IEEE Trans. Power Syst., 18, 1014, 10.1109/TPWRS.2002.804943

Suresh, 2011, Forecasting sugarcane yield of Tamilnadu using ARIMA models, Sugar Tech., 13, 23, 10.1007/s12355-011-0071-7

Wang, 2012, Stock index forecasting based on a hybrid model, Omega, 40, 758, 10.1016/j.omega.2011.07.008

Cadenas, 2010, Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model, Renew. Energy, 35, 2732, 10.1016/j.renene.2010.04.022

Babu, 2012, Predictive data mining on average global temperature using variants of ARIMA models, 256

Orhan, 2013, Real-time CHF detection from ECG signals using a novel discretization method, Comput. Biol. Med., 43, 1556, 10.1016/j.compbiomed.2013.07.015

Lapedes, 1987

Singhal, 2011, Electricity price forecasting using artificial neural networks, Int. J. Elec. Power Energy Syst., 33, 550, 10.1016/j.ijepes.2010.12.009

Chen, 2009, Using neural networks and data mining techniques for the financial distress prediction model, Expert Syst. Appl., 36, 4075, 10.1016/j.eswa.2008.03.020

Toro, 2013, A hybrid artificial intelligence model for river flow forecasting, Appl. Soft Comput., 13, 3449, 10.1016/j.asoc.2013.04.014

Chang, 2009, Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis, Appl. Soft Comput., 9, 1177, 10.1016/j.asoc.2009.03.003

Reyes, 2013, Neural networks to predict earthquakes in Chile, Appl. Soft Comput., 13, 1314, 10.1016/j.asoc.2012.10.014

Zhang, 2003, Time series forecasting using a hybrid ARIMA and neural network model, Neurocomputing, 50, 159, 10.1016/S0925-2312(01)00702-0

Khashei, 2011, A novel hybridization of artificial neural networks and ARIMA models for time series forecasting, Appl. Soft Comput., 11, 2664, 10.1016/j.asoc.2010.10.015

Arash Miranian, 2013, Day-ahead electricity price analysis and forecasting by singular spectrum analysis, IET Gener. Transm. Dis., 7, 337 L 346

mer Faruk, 2010, A hybrid neural network and ARIMA model for water quality time series prediction, Eng. Appl. Artif. Intell., 23, 586, 10.1016/j.engappai.2009.09.015

Sai, 2007, Mining stock market tendency by RS-based support vector machines, 659

Hassan, 2009, A combination of hidden Markov Model and fuzzy model for stock market forecasting, Neurocomputing, 72, 3439, 10.1016/j.neucom.2008.09.029

Olsson, 2008, Modeling real-time balancing power market prices using combined SARIMA and Markov processes, IEEE Trans. Power Syst., 23, 443, 10.1109/TPWRS.2008.920046

Ilow, 2000, Forecasting network traffic using FARIMA models with heavy tailed innovations, 3814

Liu, 2009, Volatility forecasting in the Hang Seng Index using the GARCH approach, Asia-Pacific Financial Markets, 16, 51, 10.1007/s10690-009-9086-4

Sohn, 2007, Hierarchical forecasting based on AR-GARCH model in a coherent structure, Eur. J. Operat. Res., 176, 1033, 10.1016/j.ejor.2005.08.019

Ferenstein, 2004, Modelling stock returns with AR-GARCH processes, SORT, 28, 55

Conejo, 2005, Day-ahead electricity price forecasting using the wavelet transform and ARIMA models, IEEE Trans. Power Syst., 20, 1035, 10.1109/TPWRS.2005.846054

Chen, 2011, Short-time traffic flow prediction with ARIMA-GARCH model, 607

Jain, 2007, Hybrid neural network models for hydrologic time series forecasting, Appl. Soft Comput., 7, 585, 10.1016/j.asoc.2006.03.002

Box, 1990

Gaussian maximum likelihood estimation for ARMA models. I. Time series, J. Time Series Anal. 27(6) (2006) 857–875.

Haykin, 2008

Hayes, 1996

Australian Energy Market Operator (AEMO): Price and Demand Data Sets. Available at: http://www.aemo.com.au/data/price_demand.html.

The NSE India Data. Available at: http://www.nseindia.com/products/content/equities/equities/eq_security.htm.

DeCarlo, 1997, On the meaning and use of kurtosis, Psychol. Methods, 2, 292, 10.1037/1082-989X.2.3.292