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

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.

A Legendre Neural Network for Credit Card Fraud Detection

“…The expansion block helps to augment the dimension of the input pattern through a specified expansion order, which in turn helps to unravel the inherent nonlinearity exist between the original input and its corresponding output pattern. The simple structure of the network including a learning component and an expansion block results in less computational complexity and training time compared to a MLP with hidden layers [13][14][15][16].…”

“…In this paper, the performance of a LENN is analyzed for detecting fraud in credit card transactions. In literature, a number of applications of LENN are found in prediction purpose [13][14][15][16]. In this study, the network is designed as a classification model.…”

Gold price prediction using an evolutionary pi-sigma neural network

“…Particle swarm optimization (PSO) is a meta-heuristic global optimization method based on the movement of flocks of birds and fishes [12][13][14][15]. In PSO, each individual in swarm (particle) has a position referring to a possible solution in the search space.…”

“…Differential Evolution (DE) is an evolutionary, stochastic, population-based optimization algorithm introduced by Storn and Price in 1996. In DE an optimal solution is explored from a randomly generated starting population by means of three evolutionary operations such as mutation, crossover and selection [14][15]. For each generation, the individuals of current population become target vectors.…”

PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights

“…The main contribution is the dynamic adaptation of the parameters for particle swarm optimization (PSO) used to optimize parameters for a neural network with interval type-2 fuzzy numbers weights with different T-norms and S-norms, proposed in Gaxiola et al [8] and used to perform the prediction of financial time series [9][10][11].…”