In the Big Data era users require new technology to process data stream with high speed and variety of data type. Besides the traditional stream processing technology, it is important to catch the relations inside online streaming data. Most of the data streams are composed of primitive events that produced by sensor networks, internet, social networks, etc. The semantic information inside primitive events is quite limited. In real application, people usually pay more attention to higher level information such as business logic and rules. For example, enormous events are generated in a trading system, but a fraud detecting system only care about the events that can cause frauds. Complex Event Processing (CEP) [1] is the technology that interprets and combines streams of primitive events to identify higher level composite events. CEP has been used in many areas, such as sensor networks for environmental monitoring, continuous analyzing of stocks to detect trends, etc. Predictive Complex Event Processing is the technology to identify events before they have happened, so that they can be eliminated or their effects mitigated [2]. For example, a financial institution wishes to detect frauds or a financial regulator wishes to catch illegal trading patterns in advance. Another example is to predict the traffic status in road networks and take some actions proactively to mitigate or eliminate undesired future states. Simple predictive CEP can be supported by rule-based method which means users define some patterns and the system then continuously monitor the event streams to predict future events. However, for complex cases it is not easy to define predictive CEP patterns exactly. In such circumstance, predictive analytics technology can be applied to support predictive CEP. Predictive analytics applies several statistical and data mining techniques such as clustering, classification, regression and so on. Recently, neural networks (especially deep neural networks) and Bayesian Networks (BN) are commonly used as prediction models. BN and it variations, such as Dynamic Bayesian Networks (DBN) [3], Adaptive Bayesian Networks (ABN) [4], etc., are widely used in predictive analytics because they have the following advantages: (i) they allow to express directly the fundamental qualitative relationship of direct causation, (ii) there exists mathematical methods to estimate the state of certain variables given the state of other variables, (iii) there are methods in order to explain to the user how the system came to its conclusions [5]. Currently predictive CEP with predictive analytics has some challenges. First, traditional predictive analytics methods are designed for database which means they assume all data is available at any time. However, in predictive CEP the system can only process data on single-pass and cannot control over the order of samples that arrive over time. Stream-based predictive analytics methods are needed which is more difficult. Second, the distribution of data can change over time. A model learned from historical data may not fit the new coming data well. This means real time modeling and learning algorithms are needed. In order to support predictive CEP with BN, an Evolving Bayesian Networks (EBN) model is needed that can adjust itself automatically when the distribution of data changes.