@article{Schaefer2014, author = {Sch{\"a}fer, Patrick}, title = {The BOSS is concerned with time series classification in the presence of noise}, volume = {29}, journal = {Data Mining and Knowledge Discovery}, number = {6}, publisher = {Springer}, doi = {10.1007/s10618-014-0377-7}, pages = {1505 -- 1530}, year = {2014}, language = {en} } @article{Schaefer2014, author = {Sch{\"a}fer, Patrick}, title = {Experiencing the Shotgun Distance for Time Series Analysis}, volume = {7}, journal = {Transactions on Machine Learning and Data Mining}, number = {1}, issn = {1864-9734}, pages = {3 -- 25}, year = {2014}, language = {en} } @inproceedings{Schaefer2014, author = {Sch{\"a}fer, Patrick}, title = {Towards Time Series Classification without Human Preprocessing}, volume = {8556}, booktitle = {MLDM 2014}, doi = {10.1007/978-3-319-08979-9_18}, pages = {228 -- 242}, year = {2014}, language = {en} } @article{PotamitisSchaefer2014, author = {Potamitis, Ilyas and Sch{\"a}fer, Patrick}, title = {On Classifying Insects from their Wing-Beat: New Results}, journal = {Ecology and acoustics: emergent properties from community to landscape, Paris, France}, year = {2014}, language = {en} } @article{KruegerGrunzkeGesingetal.2014, author = {Kr{\"u}ger, Jens and Grunzke, Richard and Gesing, Sandra and Breuers, Sebastian and Brinkmann, Andr{\´e} and de la Garza, Luis and Kohlbacher, Oliver and Kruse, Martin and Nagel, Wolfgang and Packschies, Lars and M{\"u}ller-Pfefferkorn, Ralph and Sch{\"a}fer, Patrick and Sch{\"a}rfe, Charlotta and Steinke, Thomas and Schlemmer, Tobias and Warzecha, Klaus Dieter and Zink, Andreas and Herres-Pawlis, Sonja}, title = {The MoSGrid Science Gateway - A Complete Solution for Molecular Simulations}, volume = {10}, journal = {Journal of Chemical Theory and Computation}, number = {6}, doi = {10.1021/ct500159h}, pages = {2232 -- 2245}, year = {2014}, language = {en} } @article{Schaefer2015, author = {Sch{\"a}fer, Patrick}, title = {Scalable time series classification}, journal = {Data Mining and Knowledge Discovery}, doi = {10.1007/s10618-015-0441-y}, pages = {1 -- 26}, year = {2015}, abstract = {Time series classification tries to mimic the human understanding of similarity. When it comes to long or larger time series datasets, state-of-the-art classifiers reach their limits because of unreasonably high training or testing times. One representative example is the 1-nearest-neighbor dynamic time warping classifier (1-NN DTW) that is commonly used as the benchmark to compare to. It has several shortcomings: it has a quadratic time complexity in the time series length and its accuracy degenerates in the presence of noise. To reduce the computational complexity, early abandoning techniques, cascading lower bounds, or recently, a nearest centroid classifier have been introduced. Still, classification times on datasets of a few thousand time series are in the order of hours. We present our Bag-Of-SFA-Symbols in Vector Space classifier that is accurate, fast and robust to noise. We show that it is significantly more accurate than 1-NN DTW while being multiple orders of magnitude faster. Its low computational complexity combined with its good classification accuracy makes it relevant for use cases like long or large amounts of time series or real-time analytics.}, language = {en} } @phdthesis{Schaefer2015, author = {Sch{\"a}fer, Patrick}, title = {Scalable Time Series Similarity Search for Data Analytics}, year = {2015}, language = {en} } @article{GrunzkeBreuersGesingetal.2013, author = {Grunzke, Richard and Breuers, Sebastian and Gesing, Sandra and Herres-Pawlis, Sonja and Kruse, Martin and Blunk, Dirk and de la Garza, Luis and Packschies, Lars and Sch{\"a}fer, Patrick and Sch{\"a}rfe, Charlotta and Schlemmer, Tobias and Steinke, Thomas and Schuller, Bernd and M{\"u}ller-Pfefferkorn, Ralph and J{\"a}kel, Ren{\´e} and Nagel, Wolfgang and Atkinson, Malcolm and Kr{\"u}ger, Jens}, title = {Standards-based metadata management for molecular simulations}, journal = {Concurrency and Computation: Practice and Experience}, doi = {10.1002/cpe.3116}, year = {2013}, language = {en} }