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.

To get started, we provide the following resources to all participants:

• Real-world datasets: ~500 datasets (multivariate time series with anomaly labels)
• Dataset generator: a dataset generator to generate even more training and testing data
• Algorithms: 66 time series anomaly detection algorithms (~25 multivariate approaches)
• Scientific publications: a list of papers and documentation for the different algorithms
• Evaluation setup: an evaluation framework (python) that automatically tests different algorithm-dataset-parameter combinations and calculates important quality and performance metrics

The schedule of this seminar should roughly be as follows :

1. Introduction session about the state-of-the-art with subsequent literature and code research
2. Brain-storming session about challenges and improvement opportunities with subsequent team building and topic selection
3. Syncronization sessions (weekly) about development progress and ideas during a longer development phase.
4. Intermediate presentation session about development ideas, preliminary findings, and challenges
5. Final presentation session about development results and evaluations
6. Scientific writing session with subsequent technical report writing phase to record seminar result

In the seminar, the participants will form teams of two students. The goal for every team then is to develop an improved multivariate time series anomaly detection algorithm that can beat the state-of-the-art algorithms (for a specific use case or in a specific scenario) in possibly many aspects. Improved in this conetxt means at least one of the following:

• More reliable: The developed algorithm is more robust against uncommon data formats and values, missing data points, etc. It can produce results, where other algorithms give up.
• More accurate: The developed algorithm can produce qualitatively better results according to quality metrics, such as area under the ROC-curve (ROC-AUC) or average precision (AP).
• More efficient: The developed algorithm can process larger datasets in shorter time and/or with lower memory requirements than the existing approaches while not (significantly) falling behind on result quality.
• More capable: The developed algorithm can detect anomalies in certain datasets or of certain types that no existing algorithms can detect.

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

The organizational details for this seminar are as follows:

• Project seminar for master students
• 6 credit points, 4 SWS
• At most 8 participants (4 teams á 2 students)
• Weekly meetings and at least two larger presentations (intermediate and final)
• Supervisors: Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock (remote), and Felix Naumann
• Appointments: Wednesdays at 17:00 - 18:30 in F-E.06, Campus II, HPI (we might change that to a better slot after the seminar has started)
• We plan to meet on-site (at HPI) for the introductory sessions and the weekly meetings while following all required regulations. Please plan your semester accordingly, because we assume your regular attendance. However, if it gets necesary due to regulation changes or other reasons, we will switch to an online mode.

On the first appointment on 27.10.2021 at 17:00 in F-E.06, we will give an introduction to the seminar and its topics. This session will be open for all of you.

Afterwards, we request you to register for this seminar firm until 29.10.2021 12:00 by sending an informal e-mail to sebastian.schmidl(at)hpi.de with the subject: "Registration to Large-Scale Time Series Analytics seminar". The email should include any prior knowledge of you that is relevant to this course (e.g. HPI courses in the data engineering, distributed computing, or machine learning area) and students that you would like to team up with in a project in case you want to join as a team (both of you need to send an e-mail). In case of more than eight registrations, we might need to choose the up to eight participants randomly. We will notify the selected participants on Friday, 29.10.2021, in the afternoon.

The grading will be based on the following tasks:

• Oral exam (mündliche Prüfung)
  • (10%) Active participation during all seminar events.
  • (30%) Presentations including:
    • (15%) Midterm presentation
    • (15%) Final presentation
• Demonstration of a developed software program (Demonstration eines erarbeiteten Computerprogramms)
  • (20%) Implementation & Documentation
  • (20%) Evaluation
  • (20%) Technical report writing (~6 pages per team / ~3 pages per person according to a two-column ACM template, e.g. \documentclass[sigconf,screen,nonacm]{acmart})

The collection of existing approaches (code, documentation, and papers), as well as relevant related work will be shared with you, once you are accepted to the course. The material for our time series anomaly detection evaluation paper is published at https://hpi-information-systems.github.io/timeeval-evaluation-paper/.