We further detect significant multifractality with true non-linear correlation remaining after correcting for spurious sources.
#Google trends democratic candidates series
All analyzed series are characterized by anti-persistence, which may be interpreted as a nervous and overreacting behavior. Specifically, multifractal detrended fluctuation analysis (MF-DFA) is applied upon data in the context of (i) president approval (polls), (ii) president on- line attention (Google Trends) and (iii) election-win probabilities (prediction markets). political time-series to gather a deeper understanding of sociophysic phenomena. This paper studies the long-range dependence and multifractal content of U.S. The results show that this method has predicted the real winner in all the elections held since 2004 and highlights that it is necessary to monitor the next elections for the presidency of the United States in November 2020 and to have more accurate information on the future results. In this article, we have taken into account the past four elections in the United States and the past five in Canada, since Google first published its search statistics in 2004. To this end, we analysed which candidate had the most Google searches in the months leading up to the polling day. To demonstrate the predictive capacity of this method, we conducted the study for two countries: the United States of America and Canada. In this article, we propose a free method to anticipate the winner of the presidential election based on this approach. These preelection polls have a different predictive capacity, despite the fact that under a Big Data approach, sources that indicate voting intention can be found. The media and election campaign managers conduct several polls in the days leading up to the presidential elections.
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