Forecasting occurrence and quantity of monthly precipitation simultaneously while accounting for complex serial correlation

Tweedie's compound Poisson regression models have been introduced in recent years to model and predict daily, monthly and seasonal precipitation data. Tweedie's compound Poisson regression analysis of precipitation data captures the relationship between the mean structure of precipitation and covariates while accounting for its right-skewness and zero-inflation appropriately, and thus extends trend analysis stage of classical time series techniques to handle right-skewed time series data with possible zero-inflation. A distinctive advantage of Tweedie's compound Poisson modelling of precipitation data is that the occurrence and quantity of precipitation can be simultaneously modelled using a single distribution; however, this approach ignores serial correlation between precipitation data observed over time. In this study, we propose to accommodate complex correlation structures of time series precipitation data using the Autoregressive Integrated Moving Average (ARIMA) models at the next stage as done in the classical time series analysis. Our analyses of monthly precipitation data in Australia demonstrated the usefulness of our two-stage approach to prediction of precipitation.

Automated summary

Tweedie's compound Poisson regression models are useful for modeling precipitation data, but they ignore the serial correlation between precipitation data. This study proposes using Autoregressive Integrated Moving Average models to accommodate the complex correlation structures of time series precipitation data.