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Comparison of Nave Bayes and Neural Networks Prediction of Euro USD Exchange Rates

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Title: Comparison of Nave Bayes and Neural Networks Prediction of Euro USD Exchange Rates


1
Comparison of Naïve Bayes and Neural Networks
Prediction of Euro USD Exchange Rates
  • This presentation will probably involve audience
    discussion, which will create action items. Use
    PowerPoint to keep track of these action items
    during your presentation
  • In Slide Show, click on the right mouse button
  • Select Meeting Minder
  • Select the Action Items tab
  • Type in action items as they come up
  • Click OK to dismiss this box
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    points entered.
  • Alec Schmid
  • Advisor Dr. Ralph Morelli

2
Problem Statement
  • Which Technique is better suited to describing
    and predicting the movement of the Euro/US Dollar
    relationship?
  • Is Artificial Intelligence applicable to
    financial situations, and can they beat the
    market?

3
Naïve Bayes
  • Probabilistic Classifier
  • Uses likelihood function to determine
    classification

4
Neural Network
  • Connectionist Learning
  • Repetition of Training

5
Progress
  • Identified general model data types
  • Researched past efforts in this field and related
    results
  • Completed 130 days of data over 15 identified
    characteristics
  • Begun Naïve Bayes classifier creation
  • Assessing Data Needs

6
Problems
  • Neural Networks generally use values from 0 to 1
    or -1 to 1
  • How accurate should the prediction be?
  • Up/Down
  • Percentage Change
  • Range of Change prediction

7
Milestones
  • Create Eurodollar Model
  • Design Program One
  • Write Program One
  • Design Program Two
  • Write Program Two
  • Train Both Programs
  • Compare Results

8
Sources
  • Websites
  • Yahoo! Finance
  • Heuristics and Artificial Intelligence in Finance
    and Investing
  • X-rates.com
  • Academic Papers
  • Designing a Neural Network for Forecasting
    Financial and Economic Time Series
  • Kaastra, Boyd.
  • Forecasting Financial Markets Using Neural
    Networks an analysis of methods and accuracy
  • Jason Kutsurelis

9
Goals for Next Review
  • Have Naïve Bayes Classifier completed.
  • Make progress on how to fit data for use with a
    neural network.
  • Determine benchmark for success
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