This study analyzes the correlation between time series characteristics and sMAPE forecasting error for the top five methods in the M4 Competition. We use two sets of financial time series with daily and monthly frequencies from the M4 Competition. Additionally, we extracted key characteristics such as trend, curvature, and seasonal strength, among others, for each series. The sMAPE error is calculated using the results provided by the competition. These variables are used in a Random Forest classifier to train ten different prediction models to identify the best forecasting method for each set of time series based on the forecasting error. The results indicate a strong correlation between the time series characteristics and the performance of the top forecasting methods in the M4 Competition, especially in the monthly time series. These series exhibit a remarkable ability to identify the most accurate forecasting method, suggesting that the characteristics of this set of series can be a determining factor in the selection process of the optimal forecasting method.