Temporal datasets provide records of the evolution and dependencies of random variables over time. Recently, there has been an increase in the application of temporal datasets in areas such as intrusion detection, fraud detection, activity recognition, etc. Interesting temporal outliers are anomalies which incorporate important or new information and contradict the causal probabilistic relationship in the domain knowledge described in a temporal dataset. One of the main objectives in data mining is to discover interesting temporal anomalous patterns, moreover, provide contextualization of the interestingness of the reported outliers. Most of the methods used to discover temporal outliers are reduction-based, losing important information in the discovery process. On the other hand, there are scarce studies about the interestingness of reported temporal outliers, many less provide contextualization of the anomaly’s cause. This thesis deals with the problem of discovering these interesting temporal outliers in datasets. We present probabilistic association rules as measures to discover interesting temporal outliers based on domain knowledge that have been learned and represented by a Dynamic Bayesian Network. Dynamic Bayesian networks are models to represent complex stochastic processes, to establish probabilistic dependencies in the feature space over time, and to capture the background knowledge in a causal relationship between features. The two probabilistic association rules, defined as low support & high confidence, and high support & low confidence, were used to identify scenarios where the discrepancies between prior and conditional probabilities are significant. Our novel approach coalesces both methods and allows discovery of interesting temporal outliers and provide contextualization in a form of relational subspaces, under the proposed methodology called “Domain Specific Temporal Anomalous Patterns.”The evaluation of the proposed methodology was done on synthetic and real temporal datasets, on the unsupervised and supervised scenario. The experimental results on temporal datasets show that our approach can detect genuine temporal outliers and provide relational subspaces to explain the probable causes of the reported outliers, with good efficiency measures. In this way, our technique becomes a state of the art method to discover interesting temporal outliers in temporal datasets and designed to give contextual information of reported outliers, this, in turn, can be used to improve our understanding of the domain knowledge and the underlying temporal data generating process.