Stock market forecasting using intrinsic time-scale decomposition in fusion with cluster based modified CSA optimized ELM

Abstract

Stock price prediction is a significant index which helps to achieve maximum benefit with minimum risk by increasing the decision making capability of financial investigators and investors. However, the problem of short term stock price prediction is a complex task due to its uncertainty, discontinuity, and random nonlinear nature. In this paper, a prediction model is proposed to predict random nonlinear stock market price using Intrinsic Time-Scale Decomposition (ITD), Cluster based Modified Crow Search Algorithm (CMCSA), and Optimized Extreme Learning Machine (OELM). ITD is adopted to decompose the non-stationary stock price data into some Proper-Rotation-Components (PRCs) and a residual component. ITD is used to convert non-stationary stock price data to stationary data which are simpler and steady for analysis. The CMCSA is proposed by modifying Crow Search Algorithm (CSA) with better capability to select optimal weight and biases of ELM. Thereafter, the optimized ELM is used to predict the PRCs and residual component individually which are then incorporated to predict closing price of short term stocks. The effectiveness of proposed CMCSA is tested and validated by solving benchmark problems. The experimental study indicates that the proposed ITD-CMCSA-OELM model outperforms the CMCSA-OELM, CSA-OELM, DE-OELM (Differential Evolution optimized ELM) and ANN models.