Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis

The Empirical Mode Decomposition (EMD) has been applied successfully in many forecasting problems. The Variational Mode Decomposition (VMD), a more effective decomposition technique has been proposed with an aim to avoid the limitations of EMD. This study focuses on two objectives i.e. day ahead stock price prediction and daily trend prediction using Robust Kernel based Extreme Learning Machine (RKELM) integrated with VMD where the kernel function parameters optimized with Differential Evolution (DE) algorithm here named as DE-VMD-RKELM. These experiments have been conducted on BSE S&P 500 Index (BSE), Hang Seng Index (HSI) and Financial Times Stock Exchange 100 Index (FTSE), and the daily price prediction performance of the proposed VMD-RKELM model is measured in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). On the other hand the daily trend prediction which is defined as a classification problem is measured in terms of Percentage of Correct Classification Accuracy (PCCA). The prediction performance of the VMD-RKELM is compared with the performance of robust Extreme Learning Machine (RELM), Extreme Learning Machine integrated with EMD (EMD-RELM). Robust Kernel Extreme Learning Machine integrated with EMD (EMD-RKELM) and two benchmark approaches i.e. Support Vector Regression (SVR) and Autoregressive Moving Average (ARMA). The trend prediction results are compared with Naive-Bayes classifier, ANN (artificial neural network), and SVM (support vector machine). The experimental results obtained from this study for price prediction as well as trend classification performance are promising and the prediction analysis illustrated in this work proves the superiority of the VMD-RKELM model over the other predictive methods. (C) 2018 Elsevier B.V. All rights reserved.