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This paper develops a novel, fully automated forecast averaging scheme, whichcombines LASSO estimation method with Principal Component Averaging (PCA).LASSO-PCA (LPCA) explores a pool of predictions based on a single model butcalibrated to windows of different sizes. It uses information criteria toselect tuning parameters and hence reduces the impact of researchers' at hockdecisions. The method is applied to average predictions of hourly day-aheadelectricity prices over 650 point forecasts obtained with various lengths ofcalibration windows. It is evaluated on four European and American markets withalmost two and a half year of out-of-sample period and compared to other semi-and fully automated methods, such as simple mean, AW/WAW, LASSO and PCA. Theresults indicate that the LASSO averaging is very efficient in terms offorecast error reduction, whereas PCA method is robust to the selection of thespecification parameter. LPCA inherits the advantages of both methods andoutperforms other approaches in terms of MAE, remaining insensitive the thechoice of a tuning parameter.

LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling