Autocorrelation correction measures include data transformation, adding lag variables, and using statistical models. Data transformation like differencing or logging can help stabilize the variance in a dataset. Adding lag variables to your model can account for autocorrelation by including previous time periods’ information. Statistical models such as Autoregressive Integrated Moving Average (ARIMA) or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) are designed to handle autocorrelation. ARIMA uses differences of observations, while GARCH models volatility clustering.
