Data Science Seminar
Lecturers: Mourad Khayati
Teaching language: English
Level: MSc students
Academic year: Fall 2020
Evaluation and Expectations
List of Papers
The seminar on data science involves presentations that cover recent topics on data science. The area of this year’s seminar is time series. In the scope of this seminar, we investigate papers that describe algorithms and techniques to perform analytics on time series data.
The goal for the students is to learn how to critically read and study research papers, how to describe a paper in a report, and how to present it in a seminar. Under supervision, students will select one paper to study and to compare with related work. This seminar aims to help students to gather in-depth knowledge of an advanced topic and develop the skills required to describe a complex problem from the time series field in the form of both a presentation, a written report, and an empirical evaluation.
IMPORTANT NOTE: The papers will be distributed on a first come first serve basis.
Evaluation and Expectations
The final grade depends on the quality of the report, presentation, reproducibility experiments, and active participation during the seminar. Each participant prepares a self-contained report of min 6 pages and gives a presentation of 30 minutes. The report should describe in detail the proposed technique(s). The report might contain a small running example, counterexample(s) if any, and should explore the extreme cases where the proposed approach would perform best and worst. The reproducibility experiments consist of reproducing the same set of experiments introduced in the paper using different datasets.
Advice on how to:
IMPORTANT NOTE: Attendance is mandatory for the two-class seminar sessions. The total number of participants will be limited to 10.
Kickoff Meeting (Onsite). Date: Tue, 22.09.2020, 10:15-12:00, room: F207
Setup and organization of the seminar, and paper assignment
The papers will be distributed on a first come first serve basis. Please use the following link to select one paper among the list of papers.
Paper & code
First Report Deadline
k-Shape: Efficient and Accurate Clustering of Time Series. SIGMOD 2015. Code: https://github.com/johnpaparrizos/kshape
Scalable, Variable-Length Similarity Search in Data Series: The ULISSE Approach, VLDB 2018. Code: http://helios.mi.parisdescartes.fr/~mlinardi/ULISSE.html
The Lernaean Hydra of Data Series Similarity Search, VLDB 2019. Code: http://helios.mi.parisdescartes.fr/~themisp/dsseval/
Data Series Progressive Similarity Search with Probabilistic Quality Guarantees. SIGMOD 2020. Code:
Neighbor Profile: Bagging Nearest Neighbors for Unsupervised Time Series Mining. ICDE 2020. Code: https://sites.google.com/view/neighbor-profile
Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values. ICDE 2020. Code: https://github.com/gieseke/bfast
Efficient Learning Interpretable Shapelets for Accurate Time Series Classification. ICDE 2018. Code: https://github.com/House1993/ELIS
Learning Individual Models for Imputation. ICDE 2019. Code: https://github.com/zaqthss/icde19-iim