Identifying and Forecasting Economic Regimes in TAC SCM

We present methods for an autonomous agent to identify dominant market conditions, such as over-supply or scarcity, and to predict market changes. 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. We use a Gaussian Mixture Model to represent the probabilities of market prices. By clustering these probabilities we identify different economic regimes. We show that the regimes so identified have properties that represent different prevailing market conditions. We then present preliminary work on methods to predict future regime transitions. A trading agent can use these predictions to make operational and strategic decisions regarding pricing, raw material acquisition, and production. We validate our method by presenting experimental results obtained with data from the Trading Agent Competition for Supply Chain Management.