Evaluation of Time-Series Forecasting Models

Medium Time-series Analysis Model Evaluation

In a time-series analysis, a financial analyst is evaluating two different models used to forecast the future prices of a stock. The first model is a simple moving average (SMA) model, while the second model is an autoregressive integrated moving average (ARIMA) model. After fitting both models to historical data, the analyst uses the Mean Absolute Percentage Error (MAPE) to assess their forecasting accuracy.

Model A has a MAPE of 8% over the test period, while Model B has a MAPE of 7%. Based on the MAPE results, which of the following conclusions can the analyst draw regarding the two models?

Model A is a better predictor of future prices than Model B.

Both models are effectively the same in terms of their predictive accuracy.

Model B demonstrates superior predictive accuracy compared to Model A.

Hint

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% Correct91%