Autocorrelation, when encountered in time series analysis, can be addressed using several methods. One common approach is to apply a transformation to the data, such as differencing or logging, which may help stabilize the variance and remove trends. Another method involves incorporating autoregressive (AR) or moving average (MA) terms into your model, essentially accounting for autocorrelation within the model itself. Alternatively, you could use models specifically designed for time series data with autocorrelation, like ARIMA or SARIMA models. It’s also important to validate that autocorrelation has been adequately addressed by checking residual plots and conducting tests like Durbin-Watson.
