Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators

This study explores the suitability of neural networks with a convolutional component as an alternative to traditional multilayer perceptrons in the domain of trend classification of cryptocurrency exchange rates using technical analysis in high frequencies. The experimental work compares the performance of four different network architectures -convolutional neural network, hybrid CNN-LSTM network, multilayer perceptron and radial basis function neural network- to predict whether six popular cryptocurrencies -Bitcoin, Dash, Ether, Litecoin, Monero and Ripple- will increase their value vs. USD in the next minute. The results, based on 18 technical indicators derived from the exchange rates at a one-minute resolution over one year, suggest that all series were predictable to a certain extent using the technical indicators. Convolutional LSTM neural networks outperformed all the rest significantly, while CNN neural networks were also able to provide good results specially in the Bitcoin, Ether and Litecoin cryptocurrencies.We would also like to acknowledge the financial support of the Spanish Ministry of Science, Innovation and Universities under grant PGC2018-096849-B-I00 (MCFin

UC3M-UKENT Stegoanalysis data archive

This repository contains the datasets used as benchmark for the steganalytical method described in the associated paper. Each collection of files is divided in two pairs, "Unmodified" being the cover object and "Modified" the object after processing with a specific steganography scheme. Comparison of both files can provide patterns useful for the steganalysis of these schemes