Predicting stock trends with time series Data Mining and Web Content Mining

Gil Rachlin, Mark Last

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Scopus citations


This paper presents a new methodology for predicting stock trends and making trading decisions based on the combination of Data Mining and Web Content Mining techniques. While research in both areas is quite extensive, inference from time series stock data and time-stamped news stories collected from the World Wide Web require further exploration. Our prediction models are based on the content of time-stamped web documents in addition to traditional Numerical Time Series Data. The stock trading system based on the proposed methodology (ADMIRAL) will be simulated and evaluated on real-world series of news stories and stocks data using several known classification algorithms. The main performance measures will be the prediction accuracy of the induced models and, more importantly, the profitability of the investments made by using system recommendations based on these predictions.

Original languageEnglish
Title of host publicationAdvances in Web Intelligence and Data Mining
EditorsMark Last, Piotr Szczepaniak, Piotr Szczepaniak, Zeev Vlvolkov, Abraham Kandel
Number of pages10
StatePublished - 27 Sep 2006

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence


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