Nord Pool day-ahead electricity price modeling and forecasting

The main focus of this thesis is a comparison of models for Nord Pool day-ahead electricity price prediction. Classical econometric models Reg-(S)ARIMA(-GARCH) and machine learning models SVR and LSTM are used to model and forecast of electricity price in the bidding areas NO2 (Norway) and DK1 (Denmark). The thesis contains analysis of electricity market in both of the countries and based on the local specifics, it explores potential contribution of exogenous variables such as air temperature, precipitation, wind speed, fossil fuels price, hydro reservoir levels and electricity consumption. The models are developed on data in years 2014 - 2016 and tested on data in years 2017 - 2018. Forecast accuracy is evaluated as Root Mean Square Error (RMSE) and statistical significance of the difference between two separate forecasts is determined based on Diebold-Mariano test. The models are compared to a Naive forecast and among each other. Even though electricity prices show very specific behaviour, the used methods are relevant even for prediction of other financial time series.