Time series classification based on qualitative space fragmentation
In knowledge discovery and data mining from time series the goal is to detect interesting patterns in the series that may help a human to better recognize the regularities in the observed variables and thereby improve the understanding of the system. Ideally, knowledge discovery algorithms use time series representations that are close to those that are used by a human. The impressive pattern recognition capabilities of the human brain help to establish connections between different time series or different parts of a single time series on the basis of their visual appearance. When dealing with time series data there are two main objectives: (i) prediction of future behavior based on past behaviors and (ii) description (explanation) of time series data. Description of time series data can be used for generalization, clustering and classification. In this paper, a novel time series classification method based on Qualitative Space Fragmentation is presented. The main characteristics of the presented method are expansion and coding of quantitative time series data together with extraction of symbolic and numeric features based on human visual perception. The expansion and coding process results in the creation of a qualitative difference vector. The qualitative difference vector conveys full information on the variation of the particular time series and can be seen as a single point in m-dimensional qualitative-space. Symbolic and numeric features based on human visual perception are extracted from the qualitative space and used for the decision tree construction that is later employed in time series classification. The application of the proposed method is demonstrated through two different case studies. In the first case study, the method was tested in the context of synthetic Control Chart Pattern data, which are time series developed for the assessment of the statistical process control. The obtained results were compared with the standard Qualitative Similarity Index method. In the second case study the method was tested in the field of analytic chemistry  polarography, an electrochemical method for analyzing solutions containing reducible or oxidizable substances.
time series classification discrete-space feature extraction qualitative domain