METRIC-BASED MODEL SELECTION FOR TIME-SERIES FORECASTING

Metric-based methods, which use unlabeled data to detect gross dif- ferences in behavior away from the training points, have recently been intro- duced for model selection, often yielding very significant improvements over alternatives (including cross-validation). We introduce extensions that take ad- vantage of the particular case of time-series data in which the task involves prediction with a horizon h. The ideas are (i) to use at t the h unlabeled exam- ples that precede t for model selection, and (ii) take advantage of the different error distributions of cross-validation and the metric methods. Experimental results establish the effectiveness of these extensions in the context of feature subset selection.