caa a22 4500 445871466 CHVBK 20180317145513.0 cr unu---uuuuu 170323e20110101xx s 000 0 eng 10.1007/s10618-010-0193-7 doi (NATIONALLICENCE)springer-10.1007/s10618-010-0193-7 Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction [Elektronische Daten] [Amy McGovern, Derek Rosendahl, Rodger Brown, Kelvin Droegemeier] We introduce an efficient approach to mining multi-dimensional temporal streams of real-world data for ordered temporal motifs that can be used for prediction. Since many of the dimensions of the data are known or suspected to be irrelevant, our approach first identifies the salient dimensions of the data, then the key temporal motifs within each dimension, and finally the temporal ordering of the motifs necessary for prediction. For the prediction element, the data are assumed to be labeled. We tested the approach on two real-world data sets. To verify the generality of the approach, we validated the application on several subjects from the CMU Motion Capture database. Our main application uses several hundred numerically simulated supercell thunderstorms where the goal is to identify the most important features and feature interrelationships which herald the development of strong rotation in the lowest altitudes of a storm. We identified sets of precursors, in the form of meteorological quantities reaching extreme values in a particular temporal sequence, unique to storms producing strong low-altitude rotation. The eventual goal is to use this knowledge for future severe weather detection and prediction algorithms. The Author(s), 2010 Temporal data mining nationallicence Multi-dimensional nationallicence Severe weather nationallicence McGovern Amy School of Computer Science, University of Oklahoma, 73019, Norman, OK, USA aut Rosendahl Derek School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Suite 5900, 73072-73071, Norman, OK, USA aut Brown Rodger NOAA/National Severe Storms Laboratory, 120 David L. Boren Blvd., 73072, Norman, OK, USA aut Droegemeier Kelvin School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Suite 5900, 73072-73071, Norman, OK, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 232-258 1384-5810 22:1-2<232 2011 22 10618 https://doi.org/10.1007/s10618-010-0193-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0193-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- McGovern Amy School of Computer Science, University of Oklahoma, 73019, Norman, OK, USA aut NATIONALLICENCE 700 1- Rosendahl Derek School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Suite 5900, 73072-73071, Norman, OK, USA aut NATIONALLICENCE 700 1- Brown Rodger NOAA/National Severe Storms Laboratory, 120 David L. Boren Blvd., 73072, Norman, OK, USA aut NATIONALLICENCE 700 1- Droegemeier Kelvin School of Meteorology, University of Oklahoma, 120 David L. Boren Blvd., Suite 5900, 73072-73071, Norman, OK, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/1-2(2011-01-01), 232-258 1384-5810 22:1-2<232 2011 22 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer