Using polls to forecast popular vote share for US presidential elections 2016 and 2020: An optimal forecast combination based on ensemble empirical model
研究用集成经验模态分解（EEMD）技术分解民调数据，结合支持向量机、神经网络和ARIMA模型预测美国总统大选普选得票率，发现组合模型优于单一模型。
This study introduces the Ensemble Empirical Mode Decomposition (EEMD) technique to forecasting popular vote shares in general elections. The technique is useful when using polling data. Our main interest in this study is shorter- and longer-term forecasting and, thus, we consider from the shortest 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. Three models, Support Vector Machine (SVM), Neural Network (NN) and ARIMA models are then used to predict the decomposition components. Subsequently, the final hybrid model is constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model is compared with the benchmark individual models: SVM, NN, and ARIMA. Finally, this is also compared to the single prediction market IOWA Electronic Markets. The results indicate that the prediction performance of combined EEMD model is better than that of the individual models.