CS 704 Artificial Intelligence Seminar (HWS 2016: Demand Forecasting)

The seminar focuses on demand forecasting. This also includes methods related to general time series forecasting that  are suituable for demand forecasting.

Organization

Goals

In this seminar, you will
  • Read, understand, and explore scientific literature
  • Summarize a current research topic in a concise report (15 pages)
  • Give a presentation about your topic (before the submission of the report)
  • Moderate a scientific discussion about a topic of one of your fellow students
  • Provide feedback to a report and a presentation of a fellow student (verbal)

Requirements

  • The report has to be written with Latex (Beginners are welcome)
  • Report and presentation have to be in English

Schedule

  • Select your preferred topics and register by 11.09.2016 (see below)
  • Attend the kickoff meeting (date will be announced)
  • You will be assigned a tutor, who provides guidance and one-to-one meetings
  • Work individually throughout the semester: explore literature, create a presentation, and write a report
  • Give your presentation and moderate a presentation of a fellow student in a block seminar at the end of the semester
  • Kick-off meeting slides: *will be available after the kickoff meeting*

Registration

Explore the list of topics below and select at least 3 topics of your preference. Send a ranked list of your selected topics via email to Jakob Huber until Sep 11, 2016. We will confirm your registration immediately. The actual topic assignment takes place at the kickoff meeting; our goal is, of course, to assign to you one of your preferred topics.

Topics

The following list represents topics that are part of this seminar. However, you are expected to explore related relevant literature on your own.

  • Forecasting during Promotions
  • Forecasting with Neural Networks
  • Bayesian Structural Time Series Forecasting
  • Short-term Forecasting (e.g. load, traffic)
  • Hierarchical Forecasting
  • Combinatorial Forecasting
  • Judgmental Forecasting

Students are free to suggest related topics of their choice.