TY - JOUR
T1 - Statistical, machine learning and deep learning forecasting methods
T2 - Comparisons and ways forward
AU - Makridakis, Spyros
AU - Spiliotis, Evangelos
AU - Assimakopoulos, Vassilios
AU - Semenoglou, Artemios Anargyros
AU - Mulder, Gary
AU - Nikolopoulos, Konstantinos
N1 - Publisher Copyright:
© Operational Research Society 2022.
PY - 2023
Y1 - 2023
N2 - The purpose of this paper is to test empirically the value currently added by Deep Learning (DL) approaches in time series forecasting by comparing the accuracy of some state-of-the-art DL methods with that of popular Machine Learning (ML) and statistical ones. The paper consists of three main parts. The first part summarizes the results of a past study that compared statistical with ML methods using a subset of the M3 data, extending however its results to include DL models, developed using the GluonTS toolkit. The second part widens the study by considering all M3 series and comparing the results obtained with that of other studies that have used the same data for evaluating new forecasting methods. We find that combinations of DL models perform better than most standard models, both statistical and ML, especially for the case of monthly series and long-term forecasts. However, these improvements come at the cost of significantly increased computational time. Finally, the third part describes the advantages and drawbacks of DL methods, discussing the implications of our findings to the practice of forecasting. We conclude the paper by discussing how the field of forecasting has evolved over time and proposing some directions for future research.
AB - The purpose of this paper is to test empirically the value currently added by Deep Learning (DL) approaches in time series forecasting by comparing the accuracy of some state-of-the-art DL methods with that of popular Machine Learning (ML) and statistical ones. The paper consists of three main parts. The first part summarizes the results of a past study that compared statistical with ML methods using a subset of the M3 data, extending however its results to include DL models, developed using the GluonTS toolkit. The second part widens the study by considering all M3 series and comparing the results obtained with that of other studies that have used the same data for evaluating new forecasting methods. We find that combinations of DL models perform better than most standard models, both statistical and ML, especially for the case of monthly series and long-term forecasts. However, these improvements come at the cost of significantly increased computational time. Finally, the third part describes the advantages and drawbacks of DL methods, discussing the implications of our findings to the practice of forecasting. We conclude the paper by discussing how the field of forecasting has evolved over time and proposing some directions for future research.
KW - artificial intelligence
KW - Forecasting
KW - neural networks
KW - performance measurement
KW - time series
UR - http://www.scopus.com/inward/record.url?scp=85137813738&partnerID=8YFLogxK
U2 - 10.1080/01605682.2022.2118629
DO - 10.1080/01605682.2022.2118629
M3 - Article
AN - SCOPUS:85137813738
SN - 0160-5682
VL - 74
SP - 840
EP - 859
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
IS - 3
ER -