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Pattern Discovery of Fuzzy Time Series for Financial Prediction
May 2006 (vol. 18 no. 5)
pp. 613-625
ASCII Text
x 
 
Chiung-Hon Leon Lee, Alan Liu, Wen-Sung Chen, "Pattern Discovery of Fuzzy Time Series for Financial Prediction," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 613-625, May, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2006.80,
author = {Chiung-Hon Leon Lee and Alan Liu and Wen-Sung Chen},
title = {Pattern Discovery of Fuzzy Time Series for Financial Prediction},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {18},
number = {5},
issn = {1041-4347},
year = {2006},
pages = {613-625},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.80},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Pattern Discovery of Fuzzy Time Series for Financial Prediction
IS - 5
SN - 1041-4347
SP613
EP625
EPD - 613-625
A1 - Chiung-Hon Leon Lee,
A1 - Alan Liu,
A1 - Wen-Sung Chen,
PY - 2006
KW - Financial data processing
KW - fuzzy sets
KW - pattern recognition
KW - time series.
VL - 18
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
A fuzzy time series data representation method based on the Japanese candlestick theory is proposed and used in assisting financial prediction. The Japanese candlestick theory is an empirical model of investment decision. The theory assumes that the candlestick patterns reflect the psychology of the market, and the investors can make their investment decision based on the identified candlestick patterns. We model the imprecise and vague candlestick patterns with fuzzy linguistic variables and transfer the financial time series data to fuzzy candlestick patterns for pattern recognition. A fuzzy candlestick pattern can bridge the gap between the investors and the system designer because it is visual, computable, and modifiable. The investors are not only able to understand the prediction process, but also to improve the efficiency of prediction results. The proposed approach is applied to financial time series forecasting problem for demonstration. By the prototype system which has been established, the investment expertise can be stored in the knowledge base, and the fuzzy candlestick pattern can also be identified automatically from a large amount of the financial trading data.

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Index Terms:
Financial data processing, fuzzy sets, pattern recognition, time series.
Citation:
Chiung-Hon Leon Lee, Alan Liu, Wen-Sung Chen, "Pattern Discovery of Fuzzy Time Series for Financial Prediction," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 613-625, May 2006, doi:10.1109/TKDE.2006.80
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