Temporal self-organization through competitive prediction
Two self-organizing principles for the competitive identification and segmentation of piecewise stationary time series are described. In the first, a neighborhood map of one step predictors competes for the data during training. The winner is granted the largest parameter update, while other predictors are allowed smaller updates, decreasing with distance from the winner on the neighborhood map. In addition to performing piecewise segmentation and identification, the technique maps similar segments of the time series as neighbors on the neighborhood map. In the second, we propose a new cost function for competitive prediction that imbeds memory in the error metric and couples the memory with the degree of competition. Performing gradient descent on the cost function yields a self-annealing system that can also perform piecewise segmentation and identification of a time series.