Vector Autoregressive Moving Average (VARMA)

A Vector Autoregressive Moving Average (VARMA) model is a statistical tool used to analyze and forecast multiple related time series data simultaneously. It combines two concepts: autoregression, which predicts future values based on past values, and moving averages, which model past errors to improve accuracy. By accounting for interactions between variables and their past behaviors, VARMA provides a comprehensive way to understand complex systems—like economic indicators or climate variables—where multiple factors influence each other over time. This makes it a powerful method for capturing the dynamics of interconnected data.