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Top1. Introduction
The chaotic, volatile and random nature of stock market has remained as main hurdle in forecasting. As an institution, it is not only impacted by macro-economic factors but also political development of a country, strategic planning of corporate houses and above all, mood of individual investors. However, over the years efficient and intelligent models have been developed by researchers to forecast it.
Broadly, there are two types of models for time series forecasting: (1) linear models, e.g. moving average, exponential smoothing, autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA); (2) non-linear models, e.g. neural network models, support vector machine, fuzzy system models etc. For better result, researchers have also developed hybrid models integrating both linear and nonlinear models (Valenzuelaa et al., 2007; Khashei, Bijari, & Ardali, 2009). Secondly, hybrid models have also been developed combining non-linear models only and different evolutionary learning algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) technique. A survey of literature will show this clearly.
A local linear wavelet neural network (LLWNN) (Chen, Yang, & Dong, 2006) is used to predict Box-Jenkins and Mackey glass time series where a hybrid training algorithm of particle swarm optimization and gradient descent method is introduced to train the model. Application of the same model is seen (Chen, Dong, & Zhao, 2005) to predict stock market indices where the parameters are optimized by using estimation of distribution algorithm. A combination of wavelet and Takagi Sugeno Kang (TSK) fuzzy rules based system is applied (Chang, Fan, & Chen, 2007) to predict financial time series data of Taiwan stock market. A fuzzy time series method based on a multiple-period modified equation derived from adaptive expectation model (Cheng, Chen, & Teoh, 2007) is used to forecast, taking the same data set. Support vector machine (Huang, Nakamori, & Wang, 2004) is used to forecast stock movement direction for NIKKEI 225 index. A least square support vector machine with a mixed kernel where genetic algorithm is used to select input features and another GA used for parameters optimization (Yu, Chen, Wang, & Lai, 2009) to predict S&P 500, DJIA and New York stock exchange. Kim and Han (2000) have applied a hybrid model, combination of artificial neural network (ANN) and GA to predict stock price indices where GA works to reduce the dimension of ANN and determine the connection weights (Kim & Han, 2000). A linear combinatory with adaptive bacterial foraging optimization is used (Majhi, Panda, Majhi, & Sahoo, 2009) to predict stock market indices. A single multiplicative neuron with cooperative random learning particle swarm optimization is applied (Zhao & Yang, 2009) to predict Mackey glass time series. A hybrid forecast method, combination of fuzzy time series and particle swarm optimization technique is used (Kuo et al., 2009) to forecast Taiwan stock exchange.