01060nas a2200133   4500000000100000008004100001260002200042100001500064700001800079700002200097700001900119245015800138520063000296       2021                            d  bIEEEaNew Orleans1 aMert Nakip1 aAlperen Helva1 aCüneyt Güzeliş1 aVolkan Rodoplu00aSubspace-Based Emulation of the Relationship Between Forecasting Error and Network Performance in Joint Forecasting-Scheduling for the Internet of Things3 a<p>We develop a novel methodology that discovers the relationship between the forecasting error and the performance of the application that utilizes the forecasts. In our methodology, an Artificial Neural Network (ANN) learns this relationship while the forecasting error is kept inside a subspace of the entire space of forecasting errors during training. We apply our methodology to the case of Joint Forecasting-Scheduling (JFS) for the Internet of Things (IoT). Our results hold potential to improve the performance of JFS in next-generation networks and can be applied to a much wider range of problems beyond IoT.</p>