Smart Data Representations: Impact on the Accuracy of Deep Neural Networks

Deep Neural Networks (DNNs) are able to solve many complex tasks with less engineering effort and better performance. However, these networks often use data for training and evaluation without investigating its representation, i.e. the form of the used data. In the present paper, we analyze the impact of data representations on the performance of DNNs using energy time series forecasting. Based on an overview of exemplary data representations, we select four exemplary data representations and evaluate them using two different DNN architectures and three forecasting horizons on real-world energy time series. The results show that, depending on the forecast horizon, the same data representations can have a positive or negative impact on the accuracy of DNNs.