Time-sensitive non-parametric Bayesian approaches for events modeling, recognition and prediction

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The modeling, recognition and prediction of events are central problems of several disciplines. These are challenging tasks that have important economic, social, technological and security advantages. Several machine learning and data mining techniques have been proposed in the past to extract useful knowledge and hidden patterns from data to recognize events and to predict future ones (e.g. opportunities, threats, etc.). The extensive use of these techniques, to tackle different tasks, has allowed practitioners to figure out that events are generally complex in nature (i.e. combine data from different sources) and that traditional events modeling, recognition and prediction approaches make generally two assumptions that are often false in real-life applications. First, that the number of classes of events is finite and known. Second, that the feature vectors representing the events are independent which is not true in the majority of the cases. Generally, events arrive sequentially and have certain dependency in time since they depend on time-varying data.