Rough computational methods for information systems
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
FLASH: A Fast Look-Up Algorithm for String Homology
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Dynamic query tools for time series data sets: timebox widgets for interactive exploration
Information Visualization
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
Forecasting volatility with neural regression: A contribution to model adequacy
IEEE Transactions on Neural Networks
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Knowledge discovery in financial databases has important implications. Decision making process on financial datasets is known to be difficult because of the complex knowledge domain and specific statistical characteristics of the data. In this paper, we investigate the decision making problem on financial datasets such as stock market fluctuations by means of financial ratio measurements while maintaining the interpretable results based on the association rules discovered. We approach this problem by considering different categories of financial ratios as input to the Rough Set model. A stepwise forecasting procedure is presented together with experimental results. The contribution of the paper is that we have successfully applied the static data mining techniques to the important financial domain and made a user friendly model that benefits individual investors in making investment decisions. We also discuss the extensions to embed the analysis and forecasting model into real time Enterprise Resources Planning (ERP) systems.