Improving support estimates by fusion of pre-election data

Miki Sirola, Jaakko Talonen, Mika Sulkava

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In this paper, Gallup results and a questionnaire in the context of a voting advice application related to the Finnish presidential election are combined. The main emphasis is on preprocessing phases where raw data is reformed to temporal data sets. We also pay attention to find optimized parameters for a merged recursive model. Aggregated data from a questionnaire was stored frequently and modified by a differential equation. The method presented in this paper allows us to visualize more accurately the daily support of each candidate before the election. The results can be used for further research such as forecasting the results and the success of presidential campaigns.
Original languageEnglish
Pages (from-to)107-118
Number of pages12
JournalInternational Scientific Journal of Computing
Volume15
Issue number2
Publication statusPublished - 30 Sep 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • data fusion
  • election
  • time series

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