Increasing the accuracy of short-term electricity price forecasting allows day-ahead power market participants to obtain a positive economic effect by bidding close to the equilibrium price. However the electricity price time-series is generally infested with extreme values due to high price volatility. This paper discusses the impact of outlier filtering on forecasting accuracy based on a recently introduced seasonal component autoregressive model. We consider such methods of outlier detection (with a priori defined cut-off parameter) as threshold, standard deviation, percentage, recursive, and moving filter on prices. It is shown that such data pre-processing often leads to the forecasting accuracy gain while the error decrease (relative to the approach without filtering) in a number of cases may reach 1.8-1.9% of the average weekly price (in absolute values). For an a priori defined cut-off parameter, the simple threshold and standard deviation filter on prices outperform other considered methods, and yield to the accuracy gain in 63% and 67% of cases, correspondingly. At the same time, in case of the out-of-sample filter parameter grid-optimization all of the methods demonstrate comparable prediction power (equal to the marginal performance). But, practically speaking, such optimization is time-consuming and cannot be carried out on unavailable future data. As an competitive alternative, we propose a combined filter on prices based on a committee machine which uses the results of individual non-optimized algorithms and is not time-consuming, but gives accuracy comparable to the best one obtained for each of the studied electricity markets and leads to forecast gain in 63% of the considered cases.