Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward-Reference-Cited by-同舟云学术

Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward

Published:2022-09-05 Issue:3 Volume:74 Page:840-859
ISSN:0160-5682
Container-title:Journal of the Operational Research Society
language:en
Short-container-title:Journal of the Operational Research Society

Author:

Makridakis Spyros¹,Spiliotis Evangelos²^ORCID,Assimakopoulos Vassilios²,Semenoglou Artemios-Anargyros¹,Mulder Gary¹,Nikolopoulos Konstantinos³

Affiliation:

1. University of Nicosia, Nicosia, Cyprus

2. National Technical University of Athens, Athens, Greece

3. Durham University Business School, Durham, UK

Funder

AWS Machine Learning Research Awards program

Publisher

Informa UK Limited

Subject

Marketing,Management Science and Operations Research,Strategy and Management,Management Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/01605682.2022.2118629

Reference76 articles.

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3. Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S. S., Salinas, D., Schulz, J., Stella, L., Türkmen, A. C. & Wang, Y. (2019). Gluonts: Probabilistic time series models in python. CoRR, abs/1906.05264.

4. Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

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