icmldemand 2015 - 2015 Workshop on Demand Forecasting and Machine Learning

Forecasting problems abound in industry while machine learning tools are being increasingly deployed to tackle them. Demand forecasting is the problem of predicting the amount of goods or services demanded by customers during some future time range. Retailers base in-stock management decisions like ordering and storage, as well as supply chain management, on demand forecasts. Energy utility companies use demand forecasting for scheduling operations, investment planning and price bidding. The data revolution creates new opportunities to improve forecast accuracy and granularity, exploiting potentially massive and heterogeneous data sources.
The focus of the workshop is on forecasting by means of data-driven techniques, with a specific emphasis on retail, energy, and transportation industries. We hope to identify the most important challenges from a business point of view, and to start a focused discussion on how to formalize and solve them by means of machine learning techniques, or on which tools are missing and require additional research efforts.
Call for Contributions
We solicit submission of extended abstracts or papers discussing high quality research on forecasting with machine learning tools. We particularly welcome submission of short papers introducing high quality (new or newly curated) datasets. A one-page extended abstract suffices for a poster submission. Both extended abstracts and research papers will be reviewed on the basis of relevance, significance, and clarity. Submissions should be formatted according to the ICML 2015 conference template. The length of abstracts and papers should not exceed 8 pages.
Submission website
https://easychair.org/conferences/?conf=icmldemand...
Important Dates
May 1, 2015 - Deadline of Submission
May 10, 2015 - Notification of Acceptance
July 11, 2015 - Workshop
Invited Speakers
Nicolas Chapados, ApSTAT Technologies
Gregory Duncan, Amazon and University of Washington
Shie Mannor, Technion
Jean Michel Poggi, Université Paris-Sud
Brian Seaman, Walmart Labs
Gilles Stoltz, CNRS
Rafal Weron, Wroclaw University of Technology
Felix Wick, Blue Yonder
Organizers
Francesco Dinuzzo (IBM Research)
Mathieu Sinn (IBM Research)
Yannig Goude (EDF R&D)
Matthias Seeger (Amazon)