A data mining approach to analyze the effect of cognitive style and subjective emotion on the accuracy of time-series forecasting

Hung Kook Park, Byoungho Song, Hyeon Joong Yoo, Dae Woong Rhee, Kang Ryoung Park, Juno Chang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data mining is finding hidden rules in given dataset using nontraditional methods. The objective is to discover useful or patterns from the given collection of data. This research investigates if the differences in accuracy of "time series forecasting" are related to the differences in one's cognitive style and subjective emotion. Two kinds of analyses were performed before applying data mining. Firstly, a statistical test was used to see if there was a positive correlation between a number of cognitive styles and subjective emotional states and the accuracy of time-series forecasting. This was not very revealing and the next step are to use a self-organizing neural network (SONN) to see if correlations between these variables could be discovered. The results showed that there were correlations but did not show whether the correlations were positive or negative. Finally data mining was applied to discover which cognitive styles and subjective emotions positively influence forecasting. It was found that subjects who have in analytic style and subjects who have a relaxed mode were more accurate in their judgments than those who do not these characteristics.

Original languageEnglish
Title of host publicationData Mining
Subtitle of host publicationTheory, Methodology, Techniques, and Applications
PublisherSpringer Verlag
Pages218-228
Number of pages11
ISBN (Print)3540325476, 9783540325475
DOIs
StatePublished - 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3755 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Cognitive style
  • Data mining
  • Decision making
  • Emotion
  • Intuitive forecasting
  • Neural network
  • Self-organizing neural network
  • Selfsupervised adaptive neural network

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