ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information

ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information Benedikt Heidrich, Kaleb Phipps, Oliver Neumann, Marian Turowski, Ralf Mikut, Veit Hagenmeyer We combine statistical methods and deep learning-based forecasting methods to enhance probabilistic forecasts. We evaluate ProbPNN empirically on more than 1000 time series from an Electricity and a Traffic data set. On these datasets, the proposed ProbPNN outperforms existing state-of-the-art methods. Abstract Probabilistic forecasts are essential for various downstream applications such as business development, traffic planning, and electrical grid balancing. Many of these probabilistic forecasts are performed on time series data that contain calendar-driven periodicities. However, existing probabilistic forecasting methods do not explicitly take these periodicities into account. Therefore, in the present paper, we introduce a deep learning-based method that considers these calendar-driven periodicities explicitly. The present paper, thus, has a twofold contribution: First, we apply statistical methods that use calendar-driven prior knowledge to create rolling statistics and combine them with neural networks to provide better probabilistic forecasts.