Temporal changes in omics events can now be routinely measured; however, current analysis methods are often inadequate, especially for multiomics experiments. We report a novel analysis method called Minardo-Model[1] - that can infer events (such as phosphorylation, dephosphorylation), and temporal ordering of events. The temporal ordering of events is inferred at a better temporal resolution than the experiment The identified events, and the temporal ordering are presented via two novel, concise and intuitive visualisation techniques called event maps and event sparklines. We tested Minardo-Model on two time series datasets, a phosphoproteomics dataset and a multiomics dataset consisting of transcriptomic, proteomic and phosphoproteomic measurements. The ordering revealed by our method correlated well with prior knowledge and indicated that our method streamlines analysis of time-series data.