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.

Spatio-temporal data is a commonly used source of information. Using machine learning to analyse this kind of data can lead to many interesting and useful insights. In this thesis project, a novel public transportation spatio-temporal dataset is explored and analysed. The dataset contains 282 GB of positional events, spanning two weeks of time, from all public transportation vehicles in Östergötland county, Sweden.  From the data exploration, three high-level problems are formulated: bus stop detection, GPS variance estimation, and arrival time prediction, also called trajectory forecasting. The bus stop detection problem is briefly discussed and solutions are proposed. Gaussian process regression is an effective method for solving regression problems. The GPS variance estimation problem is solved via the use of a mixture of Gaussian processes. A mixture of Gaussian processes is also used to predict the arrival time for public transportation buses. The arrival time prediction is from one bus stop to the next, not for the whole trajectory.  The result from the arrival time prediction is a distribution of arrival times, which can easily be applied to determine the earliest and latest expected arrival to the next bus stop, alongside the most probable arrival time. The naïve arrival time prediction model implemented has a root mean square error of 5 to 19 seconds. In general, the absolute error of the prediction model decreases over time in each respective segment. The results from the GPS variance estimation problem is a model which can compare the variance for different environments along the route on a given trajectory.