Wolf Ketter's Publications

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Identifying and Forecasting Economic Regimes in TAC SCM

Wolfgang Ketter, John Collins, Maria Gini, Alok Gupta, and Paul Schrater. Identifying and Forecasting Economic Regimes in TAC SCM. In Han La Poutré, Norman Sadeh, and Sverker Janson, editors, AMEC and TADA 2005, LNAI 3937, pp. 113–125, Springer Verlag Berlin Heidelberg, 2006.
Initial version appeared at International Joint Conference for Artificial Intelligence in Proc. of Workshop on Trading Agent Design and Analysis, pp. 53 - 60, Edinburgh, Scotland, August 1st 2005.

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Abstract

We present methods for an autonomous agent to identify dominant market conditions, such as over-supply or scarcity, and to forecast market changes. We show that market conditions can be characterized by distinguishable statistical patterns that can be learned from historic data and used, together with real-time observable information, to identify the current market regime and to forecast market changes. We use a Gaussian Mixture Model to represent the probabilities of market prices and, by clustering these probabilities, we identify different economic regimes. We show that the regimes so identified have properties that correlate with market factors that are not directly observable. We then present methods to predict regime changes. We validate our methods by presenting experimental results obtained with data from the Trading Agent Competition for Supply Chain Management.

@InCollection{KetterTADA05LNAI,
  author =   "Wolfgang Ketter and John Collins and Maria Gini and Alok Gupta and Paul Schrater",
  title =    "Identifying and Forecasting Economic Regimes in TAC SCM",
  editor =   "Han La Poutr\'{e} and Norman Sadeh and Sverker Janson",
  booktitle =    "AMEC and TADA 2005, LNAI 3937",
  pages = {113--125},
abstract = {We present methods for an autonomous agent to identify
dominant market conditions, such as over-supply or scarcity, and to
forecast market changes.  We show that market conditions can be
characterized by distinguishable statistical patterns that can be
learned from historic data and used, together with real-time
observable information, to identify the current market regime and to
forecast market changes.  We use a Gaussian Mixture Model to represent
the probabilities of market prices and, by clustering these
probabilities, we identify different economic regimes. We show that
the regimes so identified have properties that correlate with market
factors that are not directly observable.  We then present methods to
predict regime changes.  We validate our methods by presenting
experimental results obtained with data from the Trading Agent
Competition for Supply Chain Management.},
  wwwnote = {Initial version appeared at International  Joint
Conference for Artificial Intelligence in Proc. of
Workshop on Trading Agent Design and Analysis, pp. 53 -
60, Edinburgh, Scotland, August 1st 2005. },
  publisher =    "Springer Verlag Berlin Heidelberg",
  year =      {2006},
  bib2html_pubtype = {Book Chapter},
  bib2html_rescat = {Trading Agents: Supply-Chain Management},
}

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