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Predictive analytics over mobility data are of great importance since they can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example of such analytics is future location prediction, where the goal is to predict the future location of a moving object,given a look-ahead time. What is even more challenging is being able to accurately predict collective behavioural patterns of movement, such as co-movement patterns. In this paper, we provide an accurate solution to the problem of Online Prediction of Co-movement Patterns. In more detail, we split the original problem into two sub-problems, namely Future Location Prediction and Evolving Cluster Detection. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates us to match the predicted clusters with the actual ones. Finally, the accuracy of our solution is demonstrated experimentally over a real dataset from the maritime domain.
Existing datasets available to address crucial problems, such as child mortality and family planning discontinuation in developing countries, are not ample for data-driven approaches. This is partly due to disjoint data collection efforts employed ac
This paper investigates the problem of online prediction learning, where learning proceeds continuously as the agent interacts with an environment. The predictions made by the agent are contingent on a particular way of behaving, represented as a val
We consider online similarity prediction problems over networked data. We begin by relating this task to the more standard class prediction problem, showing that, given an arbitrary algorithm for class prediction, we can construct an algorithm for si
Clustering is one of the most common unsupervised learning tasks in machine learning and data mining. Clustering algorithms have been used in a plethora of applications across several scientific fields. However, there has been limited research in the
This work focuses on classification over time series data. When a time series is generated by non-stationary phenomena, the pattern relating the series with the class to be predicted may evolve over time (concept drift). Consequently, predictive mode