Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN

Prediction of Financial Time Series Data using Hybrid Evolutionary Legendre Neural Network: Evolutionary LENN

Rajashree Dash (Siksha ‘O' Anusandhan University, Bhubaneswar, India) and Pradipta Kishore Dash (Siksha ‘O' Anusandhan University, Bhubaneswar, India)
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJAEC.2016010102
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Abstract

In this paper a predictor model using Legendre Neural Network is proposed for one day ahead prediction of financial time series data. The Legendre Neural Network (LENN) is a single layer structure that possess faster convergence rate and reduced computational complexity by increasing the dimensionality of the input pattern with a set of linearly independent nonlinear functions. The parameters of the LENN model are estimated using a Moderate Random Search Particle Swarm Optimization Method (HMRPSO). The HMRPSO is a variant of PSO that uses a moderate random search method to enhance the global search ability of particles and increases their convergence rates by focusing on valuable search space regions. Training LENN using HMRPSO has also been compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of LENN for predicting the Bombay Stock Exchange and S&P 500 data sets.
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2. Literature Review

Various Artificial Neural Network (ANN) based tools have gained more popularity in financial forecasting due to their inherent capabilities to approximate any nonlinear function to a high degree of accuracy. The use of ANN to predict the behavior and tendencies of stocks has demonstrated itself to be a viable alternative to existing conventional techniques, demonstrating the average behavior of the market in the chosen prediction horizon (De Oliveira et al., 2011). It has proved that a Multi-Layer Perceptron (MLP) neural network can approximate any complex continuous function that enables us to learn any complicated relationship between the input and the output of the system (Naeini, Taremian, & Hashemi, 2010). A system of time series data analysis has been proposed in (Kozarzewski, 2010) for predicting the future values, based on wavelets preprocessing and neural networks clustering that has been tested as a tool for supporting stock market investment decisions and shows good prediction accuracy of the method. MLP neural networks are mostly used by the researchers for its inherent capabilities to approximate any non-linear function to a high degree of accuracy (QianYu, & ShaoRong, 2010; Tahersima et al., 2011; Zahedi, J., & Rounaghi, M. M. 2015; Patel, M. B., & Yalamalle, S. R. 2014; Jabin, S., 2014; Devadoss, A. V., & Ligori, T. A. A. 2013). But these models have high computational cost and need large number of iterations for its training due to the availability of hidden layer. To overcome these limitations, a different kind of ANN i.e. Functional Link ANN (FLANN) having a single neuron and single layer architecture has been proposed. In general the functional link based neural network models were single-layer ANN structure possessing higher rate of convergence and lesser computational load than those of a MLP structure (Majhi, Shalabi, & Fathi, 2005; Chakravarty, & Dash, 2009). The mathematical expression and computational calculation is evaluated as per MLP.

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