Using polls to forecast popular vote share for US presidential elections 2016 and 2020: An optimal forecast combination based on ensemble empirical model

This study introduces the Ensemble Empirical Mode Decomposition (EEMD) technique to forecasting popular vote share. The technique is useful when using polling data, which is pertinent when none of the main candidates is the incumbent. Our main interest in this study is the short- and long-term forecasting and, thus, we consider from the short forecast horizon of 1-day to three months ahead. The EEMD technique is used to decompose the election data for the two most recent US presidential elections; 2016 and 2020 US. Three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are then used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model are compared with the benchmark individual models, SVM, NN, and ARIMA. In addition, this compared to the single prediction market IOWA Electronic Markets. The results indicated that the prediction performance of EEMD combined model is better than that of individual models.