TY - JOUR
T1 - Correlation analysis of forecasting methods
T2 - The case of the M4 competition
AU - Agathangelou, Pantelis
AU - Trihinas, Demetris
AU - Katakis, Ioannis
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This commentary introduces a correlation analysis of the top-10 ranked forecasting methods that participated in the M4 forecasting competition. The “M” competitions attempt to promote and advance research in the field of forecasting by inviting both industry and academia to submit forecasting algorithms for evaluation over a large corpus of real-world datasets. After performing the initial analysis to derive the errors of each method, we proceed to investigate the pairwise correlations among them in order to understand the extent to which they produce errors in similar ways. Based on our results, we conclude that there is indeed a certain degree of correlation among the top-10 ranked methods, largely due to the fact that many of them consist of a combination of well-known, statistical and machine learning techniques. This fact has a strong impact on the results of the correlation analysis, and therefore leads to similar forecasting error patterns.
AB - This commentary introduces a correlation analysis of the top-10 ranked forecasting methods that participated in the M4 forecasting competition. The “M” competitions attempt to promote and advance research in the field of forecasting by inviting both industry and academia to submit forecasting algorithms for evaluation over a large corpus of real-world datasets. After performing the initial analysis to derive the errors of each method, we proceed to investigate the pairwise correlations among them in order to understand the extent to which they produce errors in similar ways. Based on our results, we conclude that there is indeed a certain degree of correlation among the top-10 ranked methods, largely due to the fact that many of them consist of a combination of well-known, statistical and machine learning techniques. This fact has a strong impact on the results of the correlation analysis, and therefore leads to similar forecasting error patterns.
KW - Forecasting
KW - M4
KW - Machine learning
KW - Makridakis
KW - Statistics
UR - http://www.scopus.com/inward/record.url?scp=85068570399&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2019.03.020
DO - 10.1016/j.ijforecast.2019.03.020
M3 - Comment/debate
AN - SCOPUS:85068570399
SN - 0169-2070
JO - International Journal of Forecasting
JF - International Journal of Forecasting
ER -