  Energy price forecasting is a relevant yet hard task in the field of
multi-step time series forecasting. In this paper we compare a well-known and
established method, ARMA with exogenous variables with a relatively new
technique Gradient Boosting Regression. The method was tested on data from
Global Energy Forecasting Competition 2014 with a year long rolling window
forecast. The results from the experiment reveal that a multi-model approach is
significantly better performing in terms of error metrics. Gradient Boosting
can deal with seasonality and auto-correlation out-of-the box and achieve lower
rate of normalized mean absolute error on real-world data.
