Financial Time Series Forecasting Applying Deep Learning Algorithms

Abstract

Deep learning methods can identify and analyze complex patterns and interactions within the data to optimize the trading process. This work presents a deep learning algorithm for intraday stock prices forecasting of Amazon, Inc. We focus on deep architectures such as convolutional neural networks (CNN), long short-term memory (LSTM), and densely-connected neural networks (NN). Results have shown that the combination of these architectures increases the accuracy when forecasting non-stationary time series. Furthermore, the evaluation of the proposed method has resulted in a mean absolute error (MAE) of 6.7 for one-step-ahead forecasting and 9.94 for four-step ahead forecasting.