New Upcoming Legal Jobs Free Workshop March 11 at 5 PM. Register here

    Question

    In time series forecasting, which of the following is

    true regarding the impact of autocorrelation on the model?
    A High autocorrelation in residuals indicates that the model has captured most of the information in the data. Correct Answer Incorrect Answer
    B High autocorrelation in residuals suggests that the model has failed to capture important patterns in the data. Correct Answer Incorrect Answer
    C Autocorrelation should be ignored if the data is stationary. Correct Answer Incorrect Answer
    D Autocorrelation is irrelevant for forecasting models such as ARIMA. Correct Answer Incorrect Answer
    E High autocorrelation always results in overfitting of the model. Correct Answer Incorrect Answer

    Solution

    Autocorrelation in the residuals of a time series model refers to the correlation of the residuals (errors) at different time points. If there is high autocorrelation, it suggests that the residuals are not independent of each other and that the model has not adequately captured important patterns or information from the data. A good time series model, such as ARIMA, should ideally result in residuals with no significant autocorrelation, indicating that the model has accounted for all the systematic information in the data. High autocorrelation in residuals is a sign that the model should be re-evaluated or refined to capture those patterns. Why Other Options Are Incorrect: • A: High autocorrelation in residuals suggests that important patterns have not been captured, not that the model is perfect. • C: Autocorrelation is relevant for all types of time series data, including stationary data, as it helps ensure that the model is correctly specified. • D: Autocorrelation is crucial for models like ARIMA because it informs the structure of the model. Ignoring it can result in poor forecasting performance. • E: High autocorrelation does not necessarily lead to overfitting; it generally signals that the model is missing key information, not that it is overfitting.

    Practice Next