Integrated visual analysis of patterns in time series and text data - Workflow and application to financial data analysis

In this article, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which are significantly connected in time to quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a priori method. First, based on heuristics, we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a priori method supports the discovery of such sequential temporal patterns. Then, various text features such as the degree of sentence nesting, noun phrase complexity, and the vocabulary richness, are extracted from the news items to obtain meta-patterns. Meta-patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time, cluster, and sequence visualization and analysis functionality. We provide a case study and an evaluation on financial data where we identify important future work. The workflow could be generalized to other application domains such as data analysis of smart grids, cyber physical systems, or the security of critical infrastructure, where the data consist of a combination of quantitative and textual time series data.