  Electoral forecasting is an ongoing scientific challenge with high social
impact, as current data-driven methods try to efficiently combine statistics
with economic indices and machine learning. However, recent studies in network
science pinpoint towards the importance of temporal characteristics in the
diffusion of opinion. As such, we combine concepts of micro-scale opinion
dynamics and temporal epidemics, and develop a novel macro-scale temporal
attenuation (TA) model, which uses pre-election poll data to improve
forecasting accuracy. Our hypothesis is that the timing of publicizing opinion
polls plays a significant role in how opinion oscillates, especially right
before elections. Thus, we define the momentum of opinion as a temporal
function which bounces up when opinion is injected in a multi-opinion system of
voters, and dampens during states of relaxation. We validate TA on survey data
from the US Presidential Elections between 1968-2016, and TA outperforms
statistical methods, as well the best pollsters at their time, in 10 out of 13
presidential elections. We present two different implementations of the TA
model, which accumulate an average forecasting error of 2.8-3.28 points over
the 48-year period. Conversely, statistical methods accumulate 7.48 points
error, and the best pollsters accumulate 3.64 points. Overall, TA offers
increases of 23-37% in forecasting performance compared to the state of the
art. We show that the effectiveness of TA does not drop when relatively few
polls are available; moreover, with increasing availability of pre-election
surveys, we believe that our TA model will become a reference alongside other
modern election forecasting techniques.
