Efficient Strategies of Static Features Incorporation into the Recurrent Neural Network

Recurrent neural networks (RNNs) have evolved to become one of the most powerful tools for making predictions on sequenced data, such as time series, textual data, signals, music etc. In many real-life cases, however, sequenced data are additionally characterized by static features which, due to their non-sequential nature, cannot be transferred directly into RNNs. In this paper, we discuss a method which incorporates static features into RNNs in order to influence and generalize the learning process. Furthermore, we will demonstrate that our approach significantly enhances the performance of RNNs, enabling the networks to learn the sequenced data exhibiting varying characteristics and then distinguish between them through the use of static supplementary information. Finally, we will evaluate our model against real energy consumption measurements of energy time series and verify that high-accuracy demand forecasts for different types of customers can be achieved only by way of incorporation of static features.