HLRS High Performance Computing Center Stuttgart: IKILeUS: Time Series Forecasting

Prerequisites and content levels

Prerequisites

Good experience in Python programming.
Basic knowledge in machine learning.
Basic knowledge in Linux.

Content levels

Community-target and domain-specific content: 30 hours

Learn more about course curricula and content levels.

Target audience

This course is intended for, but is not limited to, the following groups:

Postgraduate, non-computer scientists (e.g. engineers).
Researchers, financial and business sectors interested in time series analysis and market trends.
Everyone who is interested in Time Series Forecasting technology.

Learning outcomes

After this course, participants will:

learn the fundamentals of time series forecasting analysis.
gain knowledge of different algorithms and methods in time series forecasting.
gain basic skills in data preprocessing, data cleaning and preparation.
learn how to build a variety of time series forecasting models and optimize hyperparameters. In addition to interpreting and visualizing the results.
deepen your understanding of different models and algorithms through hands-on exercises and assignments.

Instructor

Layal Ali (HLRS) layal.ali(at)hlrs.de

Agenda

Week 1: Introduction into Time Series Forecasting, Long Short-Term Memory (LSTM), Exponential Smoothing.
Week 2: Autoregressive integrated moving average (ARIMA), TBATS, Multivariate Time Series Forecasting, DeepAR.
Week 3: XGboost, N_BEATS, Prophet.

Access to learning blocks will be unlocked one at a time on a weekly basis. The online seminars cover the course topics in the order listed above.

Registration-information

Register via the button at the top of this page.
We encourage you to register to the waiting list if the course is full. Places might become available.

Registration closes on October 08, 2024.

Fees

This course is free of charge.

HLRS concept for flexible learning

Flexible Learning

This course offers flexible learning, allowing you to learn at your own pace and access online course materials and cluster resources. Web-seminars are held weekly to discuss the learning modules and to answer your questions. We also provide forum channels that enable you to communicate with the lecturer and peers, as well as to share your experiences.

Learning Duration

The course is divided into multiple learning units of 10 hours each. Participants can learn the individual learning content on their own schedule. In addition, this course has fixed dates for virtual seminars and the exam.

Certificate & Attendance Confirmation

High-Performance Computing Center (HLRS) issues participants an attendance confirmation if they have attended all seminars, as well as a certificate if they have passed the exam at the end of the course.

Technical Requirement

Stable Internet connection so you can access and download the learning materials.
Access to video conferencing tool with camera and microphone for participation in regular seminars.

Contact

Junghwa Lee, phone 0711 685 87228, training(at)hlrs.de

IKILeUS

This course is offered within the framework of the project “Integrated Artificial Intelligence in Teaching at the University of Stuttgart” (IKILeUS). The aim of IKILeUS is to provide students with a comprehensive and sustainable understanding in the field of artificial intelligence (AI) and to introduce AI-based technologies that can improve teaching at the University of Stuttgart.

HLRS Training Collaborations in HPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.

Further courses

See the training overview and the Supercomputing Academy pages.

High-Performance Computing Center Stuttgart

IKILeUS: Time Series Forecasting

Location