1 / 21

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.

bedros
Download Presentation

Pattern Discovery of Fuzzy Time Series for Financial Prediction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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? • ?????

More Related