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Pattern Discovery of Fuzzy Time Series for Financial Prediction

Pattern Discovery of Fuzzy Time Series for Financial Prediction. Chiung-Hon Leon Lee, Alan Liu, Member, IEEE, and Wen-Sung Chen Presenter: Bob Crichton. Problem. Investors want to maximize profit from stock sales Need to know when to buy and sell.

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Pattern Discovery of Fuzzy Time Series for Financial Prediction

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  1. Pattern Discovery of Fuzzy Time Series for Financial Prediction Chiung-Hon Leon Lee, Alan Liu, Member, IEEE, and Wen-Sung Chen Presenter: Bob Crichton

  2. Problem • Investors want to maximize profit from stock sales • Need to know when to buy and sell

  3. Some Other Methods Used For Financial Prediction • Neural Networks • Genetic Algorithms • NeuroFuzzy • Classification and Regression Tree • Naïve Bayes • Fuzzy Time Series

  4. What’s Wrong With Other Methods? • Training of systems is not trivial, results cannot be re-used • Systems are “Black Boxes” • Models may need tuning, Investors do not have background knowledge to do so

  5. What’s Wrong With Other Methods? • Gap between prediction results & investment decisions • Investors are more concerned with reversal patterns than the actual price

  6. Authors’ proposal • Knowledge-based method, transfers data to • Comprehensible rules • Visual patterns

  7. How to represent time series data? • Symbolic Fuzzy Linguistic Variables • Computation Load is reduced • Linguistic variables can be comprehensible to investors

  8. Japanese Candlestick Theory

  9. Color Definitions • If open-close > 0 then the body color is BLACK • If open-close < 0 then the body color is WHITE • If open-close = 0 then the body color is CROSS

  10. Example Candlestick Chart

  11. Modeling the Candlestick Pattern • What’s important? • Lengths of shadow and body • Imprecise, i.e. short, long • Opening and closing values in relation to previous time period • Both use Fuzzy Linguistic variables to describe/model

  12. Membership Function For Shadow and Body Length

  13. Membership Function For Open and Close Styles

  14. Pattern Recognition Problems • Sensing Problem • Acquisition of measured values, i.e. recording stock prices over time • Feature Extraction Problem • Extract characteristic features from input data, i.e. candlestick lengths • Pattern Classification Problem • Must determine optimal decision procedures

  15. Fuzzy Sets for TAIEX • A1 = (EXTREME DECREASE) • A2 = (LARGE DECREASE) • A3 = (NORMAL DECREASE) • A4 = (SMALL DECREASE) • A5 = (SMALL INCREASE) • A6 = (NORMAL INCREASE) • A7 = (LARGE INCREASE) • A8 = (EXTREME INCREASE)

  16. TAIEX Data, Variations, and Fuzzy Sets

  17. TAIEX Forecasted Results

  18. System Prototype

  19. Authors Conclusions • Fuzzy Candlestick patterns can be used to increase efficiency of KD of financial time series. • Using system, investors can • Save and share investment experience • Increase efficiency of investment strategies

  20. Future work • Implement system on large scale

  21. Any Questions? • ?????

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