Ülgen, ToygarEl Sayed, AhmadPoyrazoğlu, Göktürk2021-03-162021-03-162020-10978-1-7281-6264-5http://hdl.handle.net/10679/7388https://doi.org/10.1109/GPECOM49333.2020.9247915The prediction of day-ahead electricity prices with higher accuracy is always helpful for the market players of the power exchange. This study was intended in the first place to find out the best time series prediction method for the selected 14 European countries. The test results of four time-series methods show that the next day prices were more in line with the previous day prices in 87% of the selected countries; Later, a classification approach is followed by 33 different features of each country to answer the question of which method would be the best for the other countries, that were not studied in this paper, would be? As a result, the support vector machine algorithm results in 57% accuracy in classifying an unknown European country to determine the best prediction method. Therefore, this paper focuses now on two correlated studies to find out the best time series prediction methods and a classification approach for selected countries.enginfo:eu-repo/semantics/restrictedAccessConference paper00085280580005110.1109/GPECOM49333.2020.9247915Time-series prediction methodsElectricity priceForecastingClassification2-s2.0-85097620515