Dynamic regime identification and prediction based on observed behavior in electronic marketplaces

Authors:
Wolfgang Ketter
Affiliations:
Department of Computer Science and Engineering, University of Minnesota
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Year:
2005

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Abstract

We present a method for an autonomous agent to identify dominant market conditions, such as oversupply or scarcity. The characteristics of economic regimes are learned from historic data and used, together with real-time observable information, to identify the current market regime and to forecast market changes. The approach is validated with data from the Trading Agent Competition for Supply Chain Management.