Time-Series Model Evaluation

Hard Time-series Analysis Model Evaluation

A financial analyst is testing a time-series model to forecast stock prices. They evaluate the model using several metrics, including the Mean Absolute Percentage Error (MAPE) and the Root Mean Squared Error (RMSE). The analyst finds that the model’s MAPE is 5% and the RMSE is 2.5. Based on these metrics, the analyst is trying to determine how well the model performs. It is understood that MAPE is scale-independent and RMSE provides a measure of the absolute fit of the model.

Which of the following statements about the model evaluation is correct?

The model's MAPE of 5% indicates that, on average, the model's predictions deviate from the actual values by 5% of the actual values, which is a very high level of accuracy.

The RMSE of 2.5 indicates that the model's predicted values are, on average, 2.5 units away from the actual values, which cannot be directly compared to the MAPE.

The MAPE and RMSE values together suggest that the model performs poorly since both metrics indicate high errors.

Hint

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