TY - JOUR
T1 - Forecasting in social settings
T2 - The state of the art
AU - Makridakis, Spyros
AU - Hyndman, Rob J.
AU - Petropoulos, Fotios
PY - 2020/1/1
Y1 - 2020/1/1
N2 - This paper provides a non-systematic review of the progress of forecasting in social settings. It is aimed at someone outside the field of forecasting who wants to understand and appreciate the results of the M4 Competition, and forms a survey paper regarding the state of the art of this discipline. It discusses the recorded improvements in forecast accuracy over time, the need to capture forecast uncertainty, and things that can go wrong with predictions. Subsequently, the review classifies the knowledge achieved over recent years into (i) what we know, (ii) what we are not sure about, and (iii) what we don't knowIn the first two areas, we explore the difference between explanation and prediction, the existence of an optimal model, the performance of machine learning methods on time series forecasting tasks, the difficulties of predicting non-stable environments, the performance of judgment, and the value added by exogenous variables. The article concludes with the importance of (thin and) fat tails, the challenges and advances in causal inference, and the role of luck.
AB - This paper provides a non-systematic review of the progress of forecasting in social settings. It is aimed at someone outside the field of forecasting who wants to understand and appreciate the results of the M4 Competition, and forms a survey paper regarding the state of the art of this discipline. It discusses the recorded improvements in forecast accuracy over time, the need to capture forecast uncertainty, and things that can go wrong with predictions. Subsequently, the review classifies the knowledge achieved over recent years into (i) what we know, (ii) what we are not sure about, and (iii) what we don't knowIn the first two areas, we explore the difference between explanation and prediction, the existence of an optimal model, the performance of machine learning methods on time series forecasting tasks, the difficulties of predicting non-stable environments, the performance of judgment, and the value added by exogenous variables. The article concludes with the importance of (thin and) fat tails, the challenges and advances in causal inference, and the role of luck.
KW - Accuracy
KW - Causality
KW - Judgment
KW - Knowns and unknowns
KW - Machine Learning
KW - Review
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85068551359&partnerID=8YFLogxK
U2 - 10.1016/j.ijforecast.2019.05.011
DO - 10.1016/j.ijforecast.2019.05.011
M3 - Article
AN - SCOPUS:85068551359
SN - 0169-2070
VL - 36
SP - 15
EP - 28
JO - International Journal of Forecasting
JF - International Journal of Forecasting
IS - 1
ER -