Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks

Neural network ensemble operators for time series forecasting

“…In dealing with the first, the NN literature has strongly argued, with supporting empirical evidence, that instead of selecting a single NN that may be susceptible to poor initial values (or model setup), it is preferable to consider a combination of different NN models (Hansen and Salamon, 1990;Zhang and Berardi, 2001;Versace et al, 2004;Barrow et al, 2010;Crone et al, 2011;Ben Taieb et al, 2012). Naftaly et al (1997) showed that ensembles across NN training initialisations of the same model can improve accuracy while removing the need for identifying and choosing the best training initialisation.…”

“…This has implications for multiple forecasting applications where NN ensembles have been used. Some examples include diverse forecasting applications such as: economic modelling and policy making (McAdam and McNelis, 2005;Inoue and Kilian, 2008), financial and commodities trading (Zhang and Berardi, 2001;Chen and Leung, 2004;Versace et al, 2004;Bodyanskiy and Popov, 2006;Yu et al, 2008), fast-moving consumer goods (Trapero et al, 2012), tourism (Pattie and Snyder, 1996), electricity load (Hippert et al, 2001;Taylor and Buizza, 2002), temperature and weather (Roebber et al, 2007;Langella et al, 2010), river flood (Campolo et al, 1999) and hydrological modelling (Dawson and Wilby, 2001), climate (Fildes and Kourentzes, 2011), and ecology (Araújo and New, 2007) to name a few. Zhang et al (1998) lists multiple other forecasting applications where they have been employed successfully.…”

An empirical methodology for developing stockmarket trading systems using artificial neural networks

“…Zirilli [69] proposes a formula based on prior knowledge of the number of unique patterns the net is expected to learn, but concedes that if you know your feature space well enough, you can usually determine this number of hidden nodes better yourself. Finally, another reasonably popular method is used by some researchers such as Kim & Lee [63] and Versace et al [70], whereby genetic algorithms are used to select between the combinatorial explosion of possible networks given choices such as network type, architecture, activation functions, input selection and preprocessing.…”

Using Genetic Algorithms for Real Estate Appraisals

“…For this reason, multiple regression analysis (MRA) has been considered for long time as the most flexible technique able to provide reliable predictions and information on real estate values and market analysis. As possible alternative, mostly in economic or financial fields, artificial neural networks (ANNs) have been tested by researchers for forecasting purposes [19][20][21][22][23][24][25].…”