2022-04-062022-04-062019Torres, J.F., Gutiérrez Avilés, D., Troncoso, A. y Martínez Álvarez, F. (2019). Random Hyper-parameter Search-Based Deep Neural Network for Power Consumption Forecasting. En IWANN 2019 : 15th International Work-Conference on Artificial Neural Networks (259-269), Gran Canaria, España: Springer.978-3-030-20520-10302-9743https://hdl.handle.net/11441/131796In this paper, we introduce a deep learning approach, based on feed-forward neural networks, for big data time series forecasting with arbitrary prediction horizons. We firstly propose a random search to tune the multiple hyper-parameters involved in the method perfor-mance. There is a twofold objective for this search: firstly, to improve the forecasts and, secondly, to decrease the learning time. Next, we pro-pose a procedure based on moving averages to smooth the predictions obtained by the different models considered for each value of the pre-diction horizon. We conduct a comprehensive evaluation using a real-world dataset composed of electricity consumption in Spain, evaluating accuracy and comparing the performance of the proposed deep learning with a grid search and a random search without applying smoothing. Reported results show that a random search produces competitive accu-racy results generating a smaller number of models, and the smoothing process reduces the forecasting error.application/pdf11engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/HyperparametersTime series forecastingDeep learninginfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess10.1007/978-3-030-20521-8_22