Process mining can now analyze continuous streams of events from different systems, not just static logs. Existing online algorithms assume events arrive in order, but this may not always be the case. This study introduces methods to handle out-of-order events in process mining, improving accuracy. By buffering or speculatively processing events, the analysis can adapt to unordered streams, as shown in experiments with real and synthetic data.