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A news-based financial time series discretization
Di Stefano, Danilo ; Pediroda, Valentino
In this paper a new method for financial time series discretization that allows to take into account qualitative features about financial indicators is proposed. Qualitative features are extracted from financial news web sites and they are inserted into the learning phase of a recursive Self Organizing Map by means of a suitable parameter derived from statistical analysis of document collections. A postprocessing phase based on unsupervised clustering by U-Matrix method leads to the actual discretization of the time series. A real case application to a stock closing price series reveals that the inclusion of qualitative features leads to a more compact discretization of the series. This could be useful if a compact coding of the series is sought, for example in the preprocessing phase of a forecasting methodology.
||recursive Self-Organizing Maps, time series analysis, statistical document analysis
||Faculty of Technology, Research Groups in Informatics
||Data processing, computer science, computer systems