Contact:

Address:	Postal Address Humboldt-Universität zu Berlin Department of Computer Science Unter den Linden 6 10099 Berlin Visiting Address Rudower Chaussee 25 12489 Berlin Room 4.407
Phone:	030-2093-41287
EMail:	patrick.schaefer hu-berlin.de
	Google Scholar
	DBLP
	0000-0003-2244-6065
	Github Profile

Teaching:

Source codes and Frameworks:

• Java: Scalable Time Series Data Analytics using SFA, BOSS, BOSS VS and WEASEL
• Python: Motiflets - Time Series Motif Discovery
• Python: Leitmotifs - Subdimensional Time Series Motif Discovery
• Python: WEASEL v1 and v2, SFA, BOSS - Time Series Classification
• Python: ClaSP - Time Series Segmentation
• C: SOFA - Exact Time Series Similarity Search Index
• aeon - A unified framework for machine learning with time series.

Selected Publications

2026

• A. Ermshaus, P. Schäfer, U. Leser. CLaP - State Detection from Time Series, PVLDB, Boston, MA, USA, 2026. (accepted)

2025

• L. Feremans, P. Schäfer, und W. Meert. MotiPlus and MotiSet: Discovering the best set of Motiflets in Time Series, ECML/PKDD 2025, Porto, Portugal (accepted)
• P. Schäfer, J. Brand, U. Leser, P. Botao, and T. Palpanas. Fast and Exact Similarity Search in less than a Blink of an Eye, ICDE 2025, Hong Kong SAR, China. (Paper, Code)
• P. Schäfer, U. Leser, Discovering Leitmotifs in Multidimensional Time Series, PVLDB, London, UK, 2025. (Paper, Webpage/Code)
• A. Ermshaus, P. Schäfer, U. Leser. CLaP - State Detection from Time Series, Prepint. (Paper-Draft)

2024

• P. Schäfer, J. Brand, U. Leser, P. Botao, T. Palpanas. Fast and Exact Similarity Search in less than a Blink of an Eye, Prepint. (Paper-Draft, Code)
• Middlehurst, M., Ismail-Fawaz, A., Guillaume, A., Holder, C., Rubio, D. G., Bulatova, G., Tsaprounis, L., Mentel, L., Walter, M., Schäfer, P., and Bagnall, A., aeon: a Python Toolkit for Learning from Time Series, Journal of Machine Learning Research, 2024. (Paper)
• Bagnall, A., Middlehurst, M., Forestier, G., Ismail-Fawaz, A., Guillaume, A., Rubio, D. G., Ermshaus, A., Schäfer, P., Papenbrock, T., Schmidl, S., and Wenig, P., An Introduction to Machine Learning from Time Series, Tutorial@ECML-PKDD, 2024.
• A. Ermshaus, P. Schäfer, U. Leser, Raising the ClaSS of Streaming Time Series Segmentation, PVLDB, Guangzhou, China, 2024. (Webpage, Paper)
• Matthew Middlehurst, Patrick Schäfer, Anthony J. Bagnall, Bake off redux: a review and experimental evaluation of recent time series classification algorithms,
  Data Min Knowl Disc, 2024. (Webpage, Paper)

2023

• A. Ermshaus, P. Schäfer, U. Leser, ClaSP: parameter-free time series segmentation, Data Min Knowl Disc, 2023. (Webpage, Paper, Code)
• P. Schäfer, U. Leser, WEASEL 2.0 - A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification, Journal Track @ ECML/PKDD 2023. (Paper, Webpage/Code)
• P. Schäfer, U. Leser, Motiflets - Simple and Accurate Detection of Motifs in Time Series, PVLDB, Vancouver, Canada, 2023. (Webpage, Paper, Code)

2022

• A. Ermshaus, P. Schäfer, U. Leser, Window Size Selection In Unsupervised Time Series Analytics: A Review and Benchmark, AALTD @ ECML/PKDD, 2022, (Paper, Webpage/Code)

2021

• P. Schäfer, A. Ermshaus, U. Leser, ClaSP - Time Series Segmentation, CIKM, Virtual, 2021(Webpage, Paper, Code)

2020

• P. Schäfer, U. Leser, TEASER: Early and Accurate Time Series Classification, ECML / PKDD, Virtual, 2020. (Webpage, Paper, Code)

2019

• S. Sprenger, P. Schäfer, Ulf Leser, BB-Tree: A practical and efficient main-memory index structure for multidimensional workloads. Int. Conf. on Extending Database Technologies (EDBT) Lisbon, Portugal, 2019. (Paper)
• S. Sprenger, P. Schäfer, Ulf Leser, BB-Tree: A main-memory index structure for multidimensional range queries (short paper). ICDE, Macau, China, 2019.

2018

• P. Schäfer, Ulf Leser, Multivariate Time Series Classification with WEASEL+MUSE, 3rd ECML/PKDD Workshop on AALTD, 2018. (Paper, Code)
• S. Sprenger, P. Schäfer, Ulf Leser, Multidimensional Range Queries on Modern Hardware, Prepint. (Paper)
• P. Schäfer, Dirk Pflugmacher, Patrick Hostert, Ulf Leser, Classifying land cover from satellite images using time series analytics, Workshop Proceedings of the EDBT/ICDT 2018. (Paper)
• P. Schäfer, Ulf Leser, Multivariate Time Series Classification with WEASEL+MUSE, Prepint. (Webpage, Paper, Code)

2017

• P. Schäfer, Ulf Leser, Fast and Accurate Time Series Classification with WEASEL, CIKM 2017. (Webpage, Paper, Code)
• P. Schäfer, Ulf Leser, Benchmarking Univariate Time Series Classifiers, BTW 2017 (Paper)

2016

• P. Schäfer, Scalable Time Series Classification, ECML/PKDD 2016, Data Min Knowl Disc 2016 (Webpage, Paper, Keynote, PPT, Code)

2015

• P. Schäfer, Scalable Time Series Classification for Data Analytics, Dissertation 2015, (Dissertation, Keynote, PPT)

2014

• P. Schäfer, The BOSS is concerned with time series classification in the presence of noise, Data Min Knowl Disc 2014, (Webpage, Paper, Code)
• P. Schäfer, Experiencing the Shotgun Distance for Time Series Analysis, Transactions MLDM 2014, (Webpage, Paper, Code)
• I. Potamitis, P. Schäfer, On classifying insects from their wing-beat: New results, Ecology and acoustics: emergent properties from community to landscape, Paris, France 2014.
• P. Schäfer, Towards Time Series Classification without Human Preprocessing, MLDM 2014, (Webpage, Paper, Code)

2013

• P. Schäfer, Shooting Audio Recordings of Insects with SFA, AmiBio Workshop 2013.
• P. Schäfer, S. Dressler, SFA : a symbolic fourier approximation and index for similarity search in high dimensional datasets, EDBT 2012, (Webpage, Paper, Code)

Organizer

Past Supervisions

• Master Thesis (in chronological order)
  • R. Moczalla, A. Ermshaus, L. Clauss, M. Hirsch, A. Ilieva, M. Wüstner
• Study Projects / Bachelor Thesis (in chronological order)
  • J. Spenger, A. Ermshaus, N. Schneider, L. Clauss, R. Moczalla, B. Leppich, F. Heinrichs, M. Hirsch, J. Brand, J.H. Stoll, J. Pfeiffer, S. Kühn, A. Hartwich, C. Mackenow, A. Hamami, N. Huseyn-zada, M. Wüstner, M. Baude, V. Munacoha, E. Kohlmann

• Time Series:
  • Classification (BOSS, WEASEL)
  • Motif Discovery (Motiflets, Leitmotifs)
  • Segmentation (ClaSP, ClaSS, ClaP)
  • Indexing / Similarity Search (SFA, SOFA)
• Vector Search

Reviewer & Program Committee:

• KDD '25
• VLDB '25 - '26
• ECML since '21 - '24
• AAAI '21 - '23
• IJCAI '20
• ADBIS '20
• AALTD since '16 - "Advanced Analytics and Learning on Temporal Data"
• LDWA '19 - "Large-Scale Data Management and Processing - Applications in Research and Industry"
• IEEE/CAA '19 - Special Issue on Time Series Classification
• TKDE, since '17
• Data Min Knowl Disc (Journal), since '16
• IEEE Cybernetics, since '18

Current Projects:

• aeon (Core developer): aeon is an open-source Python toolkit for machine learning on time series data, offering modules for forecasting, classification, clustering, similarity search, segmentation, and regression. It follows the scikit-learn API for easy integration.

Past Projects:

• sktime (Former Core developer)
• MoSGrid
• Harness
• Contrail
• XtreemFS