Suicide is a leading cause of death for a number of age groups and populations in Australia, and of important public health and policy interest. Accurately identifying trends in suicide for Australia and for smaller population sub-groups is vital for tracking our health as a nation, the effectiveness of our current policy frameworks, and the impact of a range of external shocks. Three somewhat exogenous factors that may have impacted on the level of suicide in Australia or impact on suicide into the future are the recent droughts in many parts of the country; the catastrophic 2019/20 Black Summer bushfires; and the ongoing SARS-CoV-2 or COVID-19 pandemic. Identifying whether those or similar external shocks have impacted on suicide in Australia requires an estimate of what suicide rates in Australia would have been in the absence of such shocks (the counterfactual), and then a measure of whether the observed suicide rates are in fact different to what we might otherwise have expected. This is made difficult by the apparently random variation in suicide on a daily, weekly and monthly basis (or at the very least variation for which the cause is unobservable) and other potential factors that may be impacting on suicide. The present study investigates monthly deaths by suicide in Australia from 2007-2018, with the aim of identifying trends in suicide over the period and whether there are any consistent monthly patterns. These monthly deaths are analysed separately for males and females, and by State/Territory. The mortality data on suicide and intentional self-harm have been documented and provided by the Australian Bureau of Statistics (ABS) and the Australian Institute of Health and Welfare (AIHW). Apart from exploring and identifying the overall trends and seasonality in the death rates per 100,000 persons, several best-fitting time series models are constructed in order to forecast the number of deaths beyond the observed time period. After checking the forecast accuracies of a few plausible models, a final satisfactory model was chosen for forecasting purposes.