| Summary: | Deep neural networks are rapidly gaining popularity. However, their application requires setting multiple hyper-parameters, and the performance relies strongly on this choice. We address this issue and propose a robust ex-ante hyper-parameter selection procedure for the day-ahead electricity price forecasting that, when used jointly with a tested forecast averaging scheme, yields high performance throughout three-year long out-of-sample test periods in two distinct markets. Being based on a grid search with models evaluated on long samples, the methodology mitigates the noise induced by local optimization. Forecast averaging across calibration window lengths and hyper-parameter sets allows the proposed methodology to outperform a parameter-rich least absolute shrinkage and selection operator (LASSO)-estimated model and a deep neural network (DNN) with non-optimized hyper-parameters in terms of the mean absolute forecast error.
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