Complex event processing (CEP) is widely employed to detect user-defined combinations, or patterns, of events in massive streams of incoming data. Numerous applications such as healthcare, fraud detection, and more, use CEP technologies to capture critical alerts, threats, or vital notifications. This requires that the technology meet real-time detection constraints. Multiple optimization techniques have been developed to minimize the processing time for CEP, including parallelization techniques, pattern rewriting, and more. However, these techniques may not suffice or may not be applicable when an unpredictable peak in the input event stream exceeds the system capacity. In such cases, one immediate possible solution is to drop some of the load in a technique known as load shedding. We present a novel load shedding mechanism for real-time complex event processing. Our approach uses statistics that are gathered to detect overload. The solution makes data-driven load shedding decisions to drop the less important events such that we preserve a given latency bound while minimizing the degradation in the quality of results. An extensive experimental evaluation on a broad set of real-life patterns and datasets demonstrates the superiority of our approach over the state-of-the-art techniques.