Closed antoinecarme closed 3 years ago
Reference article :
Tofallis, C (2015) "A Better Measure of Relative Prediction Accuracy for Model Selection and Model Estimation", Journal of the Operational Research Society, 66(8),1352-1362.
Can be used through the option :
self.mModelSelection_Criterion = "LnQ";
L. Tornqvist, P. Vartia and Y. O. Vartia (1985), How Should Relative Changes Be Measured? The American Statistician, 39, 43-46 http://www.jstor.org/stable/2683905
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology Alexei Botchkarev
https://arxiv.org/abs/1809.03006
Botchkarev, A. (2019) A new typology design of performance metrics to measure errors in machine learning regression algorithms. Interdisciplinary Journal of Information, Knowledge, and Management, 14, 45-79.
https://doi.org/10.28945/4184 https://www.gsrc.ca/metrics_typology2019.pdf
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4), 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001
Provided that predicted and actual values are strictly positive :
LnQ_i = ln(predicted_i / actual_i) = ln(predicted_i) – ln(actual_i)
LnQ = sum_i {LnQ_i ^ 2}
Ozone model (model selection based on LnQ) :
Air Passengers model (model selection based on LnQ):
Closing
According to :
https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error
A limitation to SMAPE is that if the actual value or forecast value is 0, the value of error will boom up to the upper-limit of error. (200% for the first formula and 100% for the second formula).
Provided the data are strictly positive, a better measure of relative accuracy can be obtained based on the log of the accuracy ratio: log(Ft / At) This measure is easier to analyse statistically, and has valuable symmetry and unbiasedness properties. When used in constructing forecasting models the resulting prediction corresponds to the geometric mean (Tofallis, 2015).