Time Series Analysis on Monthly Beer Production in Australia

Abstract

Despite Australia's high enthusiasm for beer consumption, domestic beer production falls short of meeting the demand, creating business opportunities for potential investors. In this research, the method Seasonal Autoregressive Integrated Moving Average (SARIMA) model is used to analyze a historical beer production data in Australia from January 1956 to December 1994 in each month. The optimal SARIMA model is identified through a rigorous process involving time series conversion, seasonal component removal, and data differencing to address non-stationarity. Residual analysis confirms the model's effectiveness. By dividing the dataset into training and test sets, the SARIMA model forecasts the next 24 periods, providing valuable insights for potential investors seeking optimal entry points into Australia's beer market. Results reveal a notable upward trajectory in monthly beer production, highlighting untapped growth potential within the domestic beer industry. The study's findings not only contribute to forecasting future production trends but also offer strategic guidance for investors, shedding light on sustained growth prospects in the Australian beer market.