Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail-Reference-Cited by-同舟云学术

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Deep learning with long short-term memory networks and random forests for demand forecasting in multi-channel retail

Published:2020-03-16 Issue:16 Volume:58 Page:4964-4979
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Punia Sushil¹^ORCID,Nikolopoulos Konstantinos²^ORCID,Singh Surya Prakash¹^ORCID,Madaan Jitendra K.¹,Litsiou Konstantia³

Affiliation:

1. Department of Management Studies, Indian Institute of Technology Delhi, Hauz Khas, India

2. Bangor Business School, Bangor University, Gwynedd, UK

3. Department of Marketing, Retail and Tourism, Manchester Metropolitan University Business School, Manchester, UK

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2020.1735666

Reference55 articles.

1. Forecasting aggregate retail sales:

2. Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics

3. The theta model: a decomposition approach to forecasting

4. On the Benefits of Collaborative Forecasting Partnerships Between Retailers and Manufacturers

5. Combination of forecasts for the price of crude oil on the spot market

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