Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
This work addresses data representation issues for researchers in time series forecasting, but it is incremental as it applies existing methods to a specific domain.
The paper investigates how different data representations affect the accuracy of Deep Neural Networks in energy time series forecasting, finding that the impact varies positively or negatively depending on the forecast horizon.
Deep Neural Networks 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 Deep Neural Networks 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 Deep Neural Network 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 Deep Neural Networks.