Oliver Lauwers
Research
Clustering Time Series: An Information Theoretical Approach
Clustering time series is still very much an open problem. Though many algorithms, evaluation criteria and similarity measures exist, they often inadequately treat temporal data. This research project focuses on a novel similarity metric for time series data. In state-of-the-art metrics, the time series usually is vectorized, and then a vector-distance is chosen. Quite often, some transformations are done on the data in order to find the best fit for two time series, but ultimately, a lot of temporal information is discarded. We try to generalize the cepstral distance, as explored by R. Martin, K. De Cock and prof. De Moor, to a more general class of time series. this distance measure has a fundamental description in both statistics and information theory, and makes use of the full dynamical and temporal interpretation of the time series, rather than of a vector representation of it.The ultimate goal is to have an insightful way to cluster a broad class of time series, and apply this to real-life problems as smart grid segmentation and customer relation management in banks.
Publications
301 Moved Permanently
Contact information
- Tel: +32 16 32 79 40
- Email: oliver.lauwers@esat.kuleuven.be