Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
The nature of statistical learning theory
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Making large-scale support vector machine learning practical
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Efficient mining of association rules in text databases
Proceedings of the eighth international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
IEEE Transactions on Computers
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Combining News and Technical Indicators in Daily Stock Price Trends Prediction
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Integrating Multiple Data Sources for Stock Prediction
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Correlating Time-Related Data Sources with Co-clustering
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Continuous Trend-Based Clustering in Data Streams
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
ACM Transactions on Information Systems (TOIS)
Improving stock market prediction by integrating both market news and stock prices
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Comparison of two different prediction schemes for the analysis of time series of graphs
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Analysis of time series of graphs: prediction of node presence by means of decision tree learning
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Recovery of missing information in graph sequences
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Continuous trend-based classification of streaming time series
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
Dynamic prediction of forthcoming trends in stock prices from news articles
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Making use of the big data: next generation of algorithm trading
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Automatic Domain-Specific Sentiment Lexicon Generation with Label Propagation
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Stock market prediction with data mining techniques is one of the most important issues to be investigated. In this paper, we present a system that predicts the changes of stock trend by analyzing the influence of non-quantifiable information (news articles). In particular, we investigate the immediate impact of news articles on the time series based on the Efficient Markets Hypothesis. Several data mining and text mining techniques are used in a novel way. A new statistical based piecewise segmentation algorithm is proposed to identify trends on the time series. The segmented trends are clustered into two categories, Rise and Drop, according to the slope of trends and the coefficient of determination. We propose an algorithm, which is called guided clustering, to filter news articles with the help of the clusters that we have obtained from trends. We also propose a new differentiated weighting scheme that assigns higher weights to the features if they occur in the Rise (Drop) news-article cluster but do not occur in its opposite Drop (Rise).