A hybrid neural network and optimization algorithm for forecasting and trend detection of Forex market indices

Abstract

An Autoencoder (AE) is an independent feature extractor from data samples and a deep network can be obtained by stacking several AEs. This paper presents a novel hybrid stacked Autoencoder-based Deep Kernel-based Random Vector Functional Link Network (DKRVFLN-AE) for forecasting and trend analysis of Foreign Exchange (Forex) rates. The proposed model dispenses the random choices of weights and biases, unlike the stacked Deep Random Vector Functional Link Network-AE (DRVFLN-AE), using a wavelet kernel function with strong data fitting capability based on Mercer’s condition. A modified metaheuristic Water Cycle Algorithm is used to optimize the wavelet kernel parameters and provide DKRVFLN-AE a better generalization and learning capability, faster execution speed, lower storage space, and improved accuracy to traditional deep learning Random Vector Functional Link Network and Extreme Learning Machine models. Applications of this new approach to predict exchange rates and trend analysis on three foreign exchange markets provide successful results and validate its superiority over well-known approaches like Random Vector Functional Link Networks, Support Vector Machines, Naive-Bayes, Extreme Learning Machines, and Deep Random Vector Functional Link Network.