Non-stationary series tend to have trends, seasonality, or changing variances, requiring preprocessing techniques like differencing and transformation.

Autocorrelation

Autocorrelation measures the relationship between a time series observation and its lagged values. It helps determine the presence of dependencies among observations and is an important concept in autoregressive modeling.

Autoregressive Models: Key Concepts

Order of Autoregressive Models

The order of an autoregressive model, denoted as AR(p), represents the number of lagged observations used to predict the current observation. It determines the complexity and predictive power of the model.