Learning Discrete Temporal Patterns for Time Series Forecasting

Forschungsthema/Bereich

Time Series Forecasting with Deep Learning

Typ der Abschlussarbeit

Bachelor / Master

Startzeitpunkt

05.06.2025

Bewerbungsschluss

31.07.2025

Dauer der Arbeit

6 months

Beschreibung

Background

Traditional deep learning models for time series (e.g., LSTM, Transformer) often struggle with noisy, redundant, or high-dimensional input signals. Inspired by advances in sequence modeling, this project explores a novel intermediate representation to improve forecasting performance and interpretability.

Core Idea

The thesis investigates a two-stage approach where time series data are first discretized into a learned symbolic form, followed by a sequence model trained on this compact representation. This abstraction allows the model to focus on recurring temporal motifs rather than raw data.

Why It’s Exciting

• * New representation: Extract and operate on high-level temporal units.
• * Modular & extensible: Encourages transfer learning and hybrid architectures.
• * Real-world impact: Applicable to scenarios with noise, missing data, or limited labels.
• * Evaluation: Compare against existing state-of-the-art on standard forecasting benchmarks.

Learning Outcomes

• * Implement unsupervised sequence compression techniques for time series.
• * Apply sequence models on symbolic or latent representations.
• * Conduct rigorous benchmarking and performance analysis.
• * Investigate interpretability and robustness in challenging environments.

Stretch Goals (Optional)

• * Study latent attention patterns and temporal abstraction.
• * Experiment with self-supervised objectives for time series.
• * Apply the model in domains such as energy, finance, or scientific sensor data.

Voraussetzung

Voraussetzungen an Studierende

• Solid programming skills in Python
• Basic knowledge of machine learning
• Initial experience with deep learning (e.g., PyTorch or TensorFlow)
• Interest in time series analysis and modeling
• Willingness to engage with current research literature
• Good understanding of mathematics (especially linear algebra and statistics)
• Beneficial: Experience with autoencoders or transformer models

Studiengangsbereiche

• Ingenieurwissenschaften
  Elektrotechnik & Informationstechnik
  Informatik
  Energy Engineering and Management
  Financial Engineering
  Information System Engineering and Management

Betreuung

Titel, Vorname, Name

Dr-Ing., Nicholas, Tan Jerome

Organisationseinheit

Institute for Data Processing and Electronics

E-Mail Adresse

nicholas.tanjerome@kit.edu

Link zur eigenen Homepage/Personenseite

Website

Bewerbung per E-Mail

Bewerbungsunterlagen

• Anschreiben
• Lebenslauf
• Notenauszug
• Immatrikulationsbescheinigung

E-Mail Adresse für die Bewerbung
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an nicholas.tanjerome@kit.edu


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