Information systems generate large volumes of event logs, which are used to analyze the operation of applications and services. In this case, events may arrive in the analytical circuit later than the moment of their actual occurrence and not in the original order. Such time inconsistency leads to errors in the construction of aggregated time-based indicators, while regular full recalculation of a multidimensional analytical cube requires significant computational costs. The purpose of the work is to develop an approach to updating a multidimensional cube based on the flow of log events, taking into account delays and violations of the order in which they arrive. The leading approach is to divide the final cube representation into a base cube and a compensation layer. Timely events update the base cube, while delayed events within a given compensation horizon add corrections to the compensation layer. Unique event identifiers are checked to exclude repeated event accounting. The paper presents a formal event model, describes rules for assigning events to cube cells by the occurrence time, proposes an incremental update algorithm, and provides a computational experiment on a software prototype. The experimental results show that the proposed method reduces aggregate errors compared with the window-based method and requires a smaller amount of repeated data processing compared with batch recalculation. The materials of the paper are of practical value for developing analytical pipelines for monitoring, audit, security event analysis, and user activity analysis.