AIDA Symposium and Summer School on ‘AI/ML Cutting Edge Trends'

This lecture will cover recent advances in methodologies to forecast quantities using deep neural networks with applications to autonomous agents, video streaming and network traffic forecasting. We first briefly introduce sequence prediction problems introducing the main architectural choices, such as RNNs, LSTMs and Transformers. Then we will delve into forecasting of agent motion in different settings, reporting on our recent research in social trajectory forecasting with the use of memory augmented neural networks. Finally, we will conclude with recent results on large models for time series forecasting and their application to network traffic estimation.