Deep Learning Approaches for Time-Evolving Scenarios

One of the most challenging topics of deep learning (DL) is the analysis of temporal series in complex real-world scenarios. The majority of proposed DL methods tend to simplify such environments without considering several factors. The first part of this thesis focuses on developing video surveillance and sports analytic systems, in which obstacles, social interactions, and flow directions are relevant aspects. A DL model is then proposed to predict future paths, taking into account human interactions sharing a common memory, and favouring the most common paths through belief maps. Another model is proposed, adding the possibility to consider agents' goals. This aspect is particularly relevant in sports games where players can share objectives and tactics. Both the proposed models rely on the common hypothesis that the whole amount of labelled data is available from the beginning of the analysis, without evolving. This can be a strong simplification for most real-world scenarios, where data is available as a stream and changes over time. Thus, a theoretical model for continual learning is then developed to face problems where few data come as a stream, and labelling them is a hard task. Finally, continual learning strategies are applied to one of the most challenging scenarios for DL: financial market predictions. A collection of state-of-the-art continual learning techniques are applied to financial indicators representing temporal data. Results achieved during this PhD show how artificial intelligence algorithms can help to solve real-world problems in complex and time-evolving scenarios.