TY - GEN

T1 - Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data

AU - Kuck, Mirko

AU - Crone, Sven F.

AU - Freitag, Michael

N1 - Publisher Copyright: © 2016 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2016/10/31

Y1 - 2016/10/31

N2 - Although artificial neural networks are occasionally used in forecasting future sales for manufacturing in industry, the majority of algorithms applied today are univariate statistical time series methods for level, seasonal, trend or trend-seasonal patterns. With different statistical methods created for different time series patterns, large scale applications on 10,000s of times series require automatic method selection, often done manually by human experts based on various time series characteristics, or automatically using error metrics of past performance. However, the task of selecting adequate forecasting methods can also be viewed as a supervised learning problem. For instance, a neural network can be trained as a meta-learner relating characteristic time series features to the ex post accuracy of forecasting methods for each time series. Past research has proposed different sets of time series features for meta-learning including simple statistical or information-theoretic as well as model-based features, but have neglected the use of past forecast errors. This paper studies the predictive accuracy of using different feature sets for a neural network meta-learner selecting between four statistical forecasting models, introducing error-based features (landmarkers) and statistical tests as time series meta-features. A large-scale empirical study on NN3 industry data shows promising results of including error-based feature sets in meta-learning for selecting time series forecasting models.

AB - Although artificial neural networks are occasionally used in forecasting future sales for manufacturing in industry, the majority of algorithms applied today are univariate statistical time series methods for level, seasonal, trend or trend-seasonal patterns. With different statistical methods created for different time series patterns, large scale applications on 10,000s of times series require automatic method selection, often done manually by human experts based on various time series characteristics, or automatically using error metrics of past performance. However, the task of selecting adequate forecasting methods can also be viewed as a supervised learning problem. For instance, a neural network can be trained as a meta-learner relating characteristic time series features to the ex post accuracy of forecasting methods for each time series. Past research has proposed different sets of time series features for meta-learning including simple statistical or information-theoretic as well as model-based features, but have neglected the use of past forecast errors. This paper studies the predictive accuracy of using different feature sets for a neural network meta-learner selecting between four statistical forecasting models, introducing error-based features (landmarkers) and statistical tests as time series meta-features. A large-scale empirical study on NN3 industry data shows promising results of including error-based feature sets in meta-learning for selecting time series forecasting models.

KW - Industry data

KW - Meta-learning

KW - Metafeatures

KW - Time series forecasting

UR - http://www.scopus.com/inward/record.url?scp=85007202924&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2016.7727376

DO - 10.1109/IJCNN.2016.7727376

M3 - Conference contribution/Paper

AN - SCOPUS:85007202924

T3 - Proceedings of the International Joint Conference on Neural Networks

SP - 1499

EP - 1506

BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016

Y2 - 24 July 2016 through 29 July 2016

ER -