HLNet: A Novel Hierarchical Deep Neural Network for Time Series Forecasting

Time series forecasting is a well-known application area for deep learning, in which the historical data are used to predict the future behavior of the series. Several deep learning methods have been proposed in this context, but they usually try to generate the output from the input, with no data transformation. In this paper, we introduce a novel method to improve the performance of deep learning models in time series forecasting. This method divides the model into hierarchies or levels, from more simple ones to more complex. Simpler levels handle smoothed versions of the input, while the most complex level processes the original time series. This method follows the human learning process, that is, general tasks are firstly performed and, afterwards, more precise ones are accomplished. Our proposed method has been applied with LSTM architectures, showing remarkable performance in a variety of time series. Moreover, comparisons to standard LSTM are reported.