Trend discovery in financial time series data using a case based fuzzy decision tree

Authors:
Pei-Chann Chang;Chin-Yuan Fan;Jun-Lin Lin
Affiliations:
Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan, ROC;Program of Management, MingDao University, ChangHua County 52345, Taiwan, ROC;Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 15
Cited 2

A knowledge bases fuzzy decision tree classifier for time series modeling

Fuzzy Sets and Systems
An intelligent approach to time series identification by a neural network-driven decision tree classifier

Decision Support Systems
A comparison between neural networks and decision trees based on data from industrial radiographic testing

Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Hybrid Intelligent Systems for Stock Market Analysis

ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees

EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
A TSK type fuzzy rule based system for stock price prediction

Expert Systems with Applications: An International Journal
Evolving and clustering fuzzy decision tree for financial time series data forecasting

Expert Systems with Applications: An International Journal
Immune K-means and negative selection algorithms for data analysis

Information Sciences: an International Journal
A hybrid coevolutionary algorithm for designing fuzzy classifiers

Information Sciences: an International Journal
Type-1 and type-2 fuzzy inference systems as integration methods in modular neural networks for multimodal biometry and its optimization with genetic algorithms

Information Sciences: an International Journal
Combining SOM and fuzzy rule base for flow time prediction in semiconductor manufacturing factory

Applied Soft Computing
Evolving least squares support vector machines for stock market trend mining

IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Mining stock market tendency using GA-Based support vector machines

WINE'05 Proceedings of the First international conference on Internet and Network Economics
Coupling fuzzy modeling and neural networks for river flood prediction

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

An improved CART decision tree for datasets with irrelevant feature

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Decision forest: an algorithm for classifying multivariate time series

International Journal of Business Intelligence and Data Mining

Quantified Score

Hi-index	12.05

Visualization

Abstract

In recent years, many attempts have been applied to predict the behavior of stock price's movement. However, these attempts could not build an accurate and efficient stock trading system owing to the high dimensionality and non-stationary variations of stock price within a large historic database. To solve this problem, this paper applies fuzzy logic as a data mining process to generate decision trees from a stock database containing historical information. There are many attributes in the stock database and often it is impossible to develop a mathematical model to classify the data. This paper establishes a novel case based fuzzy decision tree model to identify the most important predicting attributes, and extract a set of fuzzy decision rules that can be used to predict the time series behavior in the future. The fuzzy decision tree generated from the stock database is then converted to fuzzy rules that can be further applied in decision-making of stock price's movement based on its current condition. To demonstrate the effectiveness of the CBFDT model, it is experimentally compared with other approaches on Standard & Poor's 500 (S&P500) index and some stocks in S&P500. The overall performances of CBFDT model are very convincing thus it provides a new implication of research in dealing with financial time series data.