TimeSeer: Scagnostics for high-dimensional time series
| Autor: | Anushka Anand, Tuan Nhon Dang, Leland Wilkinson |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Multivariate statistics
Computer science Machine learning computer.software_genre Sensitivity and Specificity Set (abstract data type) User-Computer Interface Imaging Three-Dimensional Image Interpretation Computer-Assisted Computer Graphics Computer Simulation Time series Models Statistical Series (mathematics) Euclidean space business.industry Reproducibility of Results Pattern recognition Computer Graphics and Computer-Aided Design Skewness Signal Processing Outlier Multivariate Analysis Pairwise comparison Computer Vision and Pattern Recognition Artificial intelligence business computer Algorithms Software |
| Zdroj: | IEEE transactions on visualization and computer graphics. 19(3) |
| ISSN: | 1941-0506 |
| Popis: | We introduce a method (Scagnostic time series) and an application (TimeSeer) for organizing multivariate time series and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional euclidean space. These characterizations include measures, such as, density, skewness, shape, outliers, and texture. Working directly with these Scagnostic measures, we can locate anomalous or interesting subseries for further analysis. Our application is designed to handle the types of doubly multivariate data series that are often found in security, financial, social, and other sectors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|