Evaluating Residuals in ARIMA Models

Very Easy Time-series Analysis Model Evaluation

In time-series analysis, model evaluation is crucial for determining the effectiveness and accuracy of the models used for forecasting. Suppose you have developed an autoregressive integrated moving average (ARIMA) model for predicting monthly sales data of a company. To assess the performance of your model, you decide to analyze the residuals from your predictions.

Which of the following statements is most accurate regarding the evaluation of residuals in your ARIMA model?

Residuals should exhibit a non-random pattern to indicate a good model fit.

Residuals should be normally distributed and exhibit no autocorrelation.

Residuals must be equal in absolute terms for all observations.

Hint

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