A Hybrid Moth-Flame Optimization And Extreme Learning...

A Hybrid Moth-Flame Optimization and Extreme Learning Machine Model for Financial Forecasting Abstract—In this work, a system for stock market prediction is proposed based on a hybrid moth-flame optimizer (MFO) and extreme learning machine (ELM). ELM is also a promising method for data regression and classification and has the advantage of fast training time, but it always requires a huge number of nodes in the hidden layer. The usage of a large number of nodes in the hidden layer increases the test/evaluation time of the net, and there is no grantee of optimality of the setting of weights and biases on the hidden layer. MFO is a recently proposed promising optimization algorithm that mimics the moving behavior of moths. MFO is exploited†¦show more content†¦ELM is used as supervised learning method for SLFN method. ELM has high accuracy and fast prediction speed while solving numerous real-life problems [2], [6]. ELM randomly selects the input weights and hidden layer biases instead of fully tuning all the internal parameters such as gradient-based algorithms. ELM could analytically determine the output weights [2]. Due to random choosing of input weights and hidden layer biases, ELM needs more hidden neurons than gradient-based learning algorithms [7], [8]. The bio-inspired algorithms were used in optimizing ELM to overcome its drawbacks. In [7] differential evolution (DE) algorithm was applied to select input weights and biases to determine the output weights of ELM. DE-ELM achieved good generalization performance with a compact structure. In [9] DE-ELM was used for the classification of hyperspectral images, and it improved classification accuracy and computation time. In [10] Evolutionary ELM based on PSO algorithm is proposed, and PSO algorithm improved the performance of traditional ELM. In [11] a new method combined ELM with an improved PSO called is proposed to improve the convergence performance of ELM. In [12] an evolutionary approach for constituting extreme learning machine (ELM) ensembles is introduced to direct the selection of base learners and produced an optimal solution with ensemble size control. In [13] Genetic ensemble of