Which Forecast Method Provides More Accurate Results?

Which forecast method, the naive method or using the average of all historical data, appears to provide more accurate forecasts for the historical data? Explain.

Naive Method:

MAE = 5.00

MSE = 29.40

MAPE = 36.42%

Using Average of All Historical Data:

MAE = 3.04

MSE = 12.19

MAPE = 23.65%

Answer:

The forecast method using the average of all historical data is more accurate for this time series data than the naive method. This is confirmed by lower values of all forecast accuracy indicators, including MAE, MSE, and MAPE.

Accuracy of a forecast method is evaluated based on Key Performance Indicators such as MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error). Lower values of MAE, MSE, and MAPE indicate higher accuracy in forecasting.

In this case, the forecast accuracy indicators show that using the average of all historical data yields better results. The MAE, MSE, and MAPE values are lower for this method compared to the naive method, demonstrating its superior accuracy.

It's worth noting that each accuracy measure provides unique insights into forecast errors. MAE focuses on average absolute error, treating all errors equally. MSE gives more weight to larger errors by calculating the average squared error. MAPE expresses the forecast error as a percentage, allowing for comparisons across different time series.