TY - JOUR A1 - Grunzke, Richard A1 - Breuers, Sebastian A1 - Gesing, Sandra A1 - Herres-Pawlis, Sonja A1 - Kruse, Martin A1 - Blunk, Dirk A1 - de la Garza, Luis A1 - Packschies, Lars A1 - Schäfer, Patrick A1 - Schärfe, Charlotta A1 - Schlemmer, Tobias A1 - Steinke, Thomas A1 - Schuller, Bernd A1 - Müller-Pfefferkorn, Ralph A1 - Jäkel, René A1 - Nagel, Wolfgang A1 - Atkinson, Malcolm A1 - Krüger, Jens T1 - Standards-based metadata management for molecular simulations JF - Concurrency and Computation: Practice and Experience Y1 - 2013 U6 - https://doi.org/10.1002/cpe.3116 ER - TY - GEN A1 - Schäfer, Patrick T1 - Bag-Of-SFA-Symbols in Vector Space (BOSS VS) N2 - Time series classification mimics the human understanding of similarity. When it comes to larger datasets, state of the art classifiers reach their limits in terms of unreasonable training or testing times. One representative example is the 1-nearest-neighbor DTW classifier (1-NN DTW) that is commonly used as the benchmark to compare to and has several shortcomings: it has a quadratic time and it degenerates in the presence of noise. To reduce the computational complexity lower bounding techniques or recently a nearest centroid classifier have been introduced. Still, execution times to classify moderately sized datasets on a single core are in the order of hours. We present our Bag-Of-SFA-Symbols in Vector Space (BOSS VS) classifier that is robust and accurate due to invariance to noise, phase shifts, offsets, amplitudes and occlusions. We show that it is as accurate while being multiple orders of magnitude faster than state of the art classifiers. Using the BOSS VS allows for mining massive time series datasets and real-time analytics. T3 - ZIB-Report - 15-30 KW - Time Series KW - Classification KW - Data Mining Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-54984 SN - 1438-0064 ER -