A deep increasing–decreasing-linear neural network for financial time series prediction

Several neural network models have been proposed in the literature to predict the future behavior of financial time series. However, an intrinsic limitation arises from this particular prediction task with modeling via neural networks, since the prediction, when sampled in daily frequency, have 1-step-ahead delay with respect to real time series observations. In order to overcome such drawback, we present a deep increasing–decreasing-linear neural network (wherein each layer is composed of a set of increasing–decreasing-linear processing units) to predict the behavior of financial time series. In addition, we present a learning process to train the proposed model using a descending gradient-based approach. In order to assess the model’s prediction performance, we use twelve financial time series from relevant stock markets around the world. The obtained results show that the proposed model have competitiveness, in terms of predictive performance, and have better effectiveness when compared to recent models presented in the literature of time series prediction.