Large-Scale Time Series Analytics (Wintersemester 2021/2022)
Lecturer:
Prof. Dr. Felix Naumann
(Information Systems)
,
Sebastian Schmidl
(Information Systems)
,
Phillip Wenig
(Information Systems)
Course Website:
General Information
- Weekly Hours: 4
- Credits: 6
- Graded:
yes
- Enrolment Deadline: 01.10.2021 - 22.10.2021
- Teaching Form: Project / Seminar
- Enrolment Type: Compulsory Elective Module
- Course Language: English
- Maximum number of participants: 8
Programs, Module Groups & Modules
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-K Konzepte und Methoden
- DATA: Data Analytics
- HPI-DATA-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-S Spezialisierung
- SCAL: Scalable Data Systems
- HPI-SCAL-K Konzepte und Methode
- SCAL: Scalable Data Systems
- HPI-SCAL-T echniken und Werkzeuge
- SCAL: Scalable Data Systems
- HPI-SCAL-S Spezialisierung
Description
In this project seminar, we investigate and improve anomaly detection algorithms for multivariate time series. The participants will receive a broad selection of state-of-the-art anomaly detection algorithms (with code and papers) and are then challenged to beat these approaches in runtime and/or precision. Techniques that we consider for this task involve, inter alia, workload parallelization and distribution, streaming, ensambling, machine learning, and hybridization.
Requirements
For this seminar, participants need to be able to program fluently in at least one higher-level (functional or object-oriented) programming language, such as Java/Scala/Kotlin, Python, C++, Ruby etc. The seminar also requires some fundamental knowledge about basic algorithms and data structures.
The following skills are a plus, but can also be learned during the seminar:
- Experience in Python, Numpy, and PyTorch, because most of the existing algorithms are implemented in these technologies
- Knowledge about the development of efficient and scalable algorithms (ideally Distributed Data Management)
- Some fundamental understanding of data mining and machine learning algorithms
Learning
Project seminar with weekly meetings
Seminar kickoff and regular meetings will be held in presence at HPI
Examination
Oral exam consisting of a midterm and a final presentation and demonstration of a developed software program by handing in source code and documentation
Mündliche Prüfung, (30-45 Min.) und Demonstration eines erarbeiteten Computerprogramms (30 Min.)
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