Data mining using extensions of the rough set model
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Structures in Logic and Computer Science, A Selection of Essays in Honor of Andrzej Ehrenfeucht
Software Agents and Soft Computing: Towards Enhancing Machine Intelligence, Concepts and Applications
Induction of Classification Rules by Granular Computing
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Time Dependent Directional Profit Model for Financial Time Series Forecasting
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Rough Neural Network of Variable Precision
Neural Processing Letters
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
Statistical fuzzy interval neural networks for currency exchange rate time series prediction
Applied Soft Computing
Rough Set Model Selection for Practical Decision Making
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
On reduct construction algorithms
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Feature selection with adjustable criteria
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Learning Optimal Parameters in Decision-Theoretic Rough Sets
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
WSEAS Transactions on Information Science and Applications
Improving trading systems using the RSI financial indicator and neural networks
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
A multi-agent decision-theoretic rough set model
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Piecewise cloud approximation for time series mining
Knowledge-Based Systems
Rough set analysis on call center metrics
Applied Soft Computing
Expert Systems with Applications: An International Journal
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
Analysis of data-driven parameters in game-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Time series forecasting through rule-based models obtained via rough sets
Artificial Intelligence Review
Approximations and uncertainty measures in incomplete information systems
Information Sciences: an International Journal
Fundamenta Informaticae - Advances in Rough Set Theory
Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Alternative rule induction methods based on incremental object using rough set theory
Applied Soft Computing
An intelligent supplier evaluation, selection and development system
Applied Soft Computing
Core set analysis in inconsistent decision tables
Information Sciences: an International Journal
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We investigate the use of the rough set model for financial time-series data analysis and forecasting. The rough set model is an emerging technique for dealing with vagueness and uncertainty in data. It has many advantages over other techniques, such as fuzzy sets and neural networks, including attribute reduction and variable partitioning of data. These characteristics can be very useful for improving the quality of results from data analysis. We demonstrate a rough set data analysis model for the discovery of decision rules from time series data for example, the New Zealand stock exchanges. Rules are generated through reducts and can be used for future prediction. A unique ranking system for the decision rules based both on strength of the rule and stability of the rule is used in this study. The ranking system gives the user confidence regarding their market decisions. Our experiment results indicate that the forecasting of future stock index values using rough sets obtains decision ruleswith high accuracy and coverage.