Published on Sat Aug 08 2020

Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions

Wilfredo Tovar

In the big data era, deep learning and intelligent data mining technique have been applied by researchers in various areas. This paper proposes the implementation of a generative adversarial network (GAN), which is composed by a bi-directional Long short-term memory(LSTM) and convolutional neural network(CNN) The proposed solution outperforms preceding models.

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Abstract

In the big data era, deep learning and intelligent data mining technique solutions have been applied by researchers in various areas. Forecast and analysis of stock market data have represented an essential role in today's economy, and a significant challenge to the specialist since the market's tendencies are immensely complex, chaotic and are developed within a highly dynamic environment. There are numerous researches from multiple areas intending to take on that challenge, and Machine Learning approaches have been the focus of many of them. There are multiple models of Machine Learning algorithms been able to obtain competent outcomes doing that class of foresight. This paper proposes the implementation of a generative adversarial network (GAN), which is composed by a bi-directional Long short-term memory (LSTM) and convolutional neural network(CNN) referred as Bi-LSTM-CNN to generate synthetic data that agree with existing real financial data so the features of stocks with positive or negative trends can be retained to predict future trends of a stock. The novelty of this proposed solution that distinct from previous solutions is that this paper introduced the concept of a hybrid system (Bi-LSTM-CNN) rather than a sole LSTM model. It was collected data from multiple stock markets such as TSX, SHCOMP, KOSPI 200 and the S&P 500, proposing an adaptative-hybrid system for trends prediction on stock market prices, and carried a comprehensive evaluation on several commonly utilized machine learning prototypes, and it is concluded that the proposed solution approach outperforms preceding models. Additionally, during the research stage from preceding works, gaps were found between investors and researchers who dedicated to the technical domain.

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