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 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.