A neighborhood map of competing one step predictors for piecewise segmentation and identification of time series
A new off-line technique for the competitive identification of piecewise stationary time series is described. 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.