Learning Discrete Temporal Patterns for Time Series Forecasting

Research topic/area

Time Series Forecasting with Deep Learning

Type of thesis

Bachelor / Master

Start time

05.06.2025

Application deadline

31.07.2025

Duration of the thesis

6 months

Description

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.

Requirement

Requirements for students

• 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

Faculty departments

• Engineering sciences
  Electrical engineering & information technologies
  Informatics
  Energy Engineering and Management
  Financial Engineering
  Information System Engineering and Management

Supervision

Title, first name, last name

Dr-Ing., Nicholas, Tan Jerome

Organizational unit

Institute for Data Processing and Electronics

Email address

nicholas.tanjerome@kit.edu

Link to personal homepage/personal page

Website

Application via email

Application documents

• Cover letter
• Curriculum vitae
• Grade transcript
• Certificate of enrollment

E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an nicholas.tanjerome@kit.edu


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