Analyzing trends by symbolic episode representation and sequence alignment

Data analysis is often associated with quantitative techniques because of the large amount of data and easy-to-use statistical tools. Qualitative trend analysis (QTA) techniques always have to be guided with some data reduction method, e.g. principal component analysis (PCA) or segmentation, and the preprocessed, lowered size data can be analyzed for further aims. Derivative-based segmentation methods are presented which are popular in fault diagnosis. If there is an adequate distance measure, one is able to qualify, compare or classify different time series. This article proposes segmentation-based alignment techniques based on dynamic distance measure: time warping (DTW) and a developed one, which uses pairwise sequence alignment -a common tool in bioinformatics -to align triangular episode sequences. Both techniques highly depend on the pre-defined distance or similarity measure between the trends because they try to find the minimal distance or maximal similarity path. These two techniques are compared and qualified on handwriting data based case study. It has been shown that symbolic episode segmentation based sequence alignment aided by prior knowledge of the operators can handle qualitative trend analysis and thus it is able to monitor and qualify operating processes.