Index

TSI Challenge Summer 2024: Heat & Water Demand Forecasting

The Time Series Intelligence group from the Pattern Recognition Lab offers a 5/10 ECTs project in a challenge format. This is a “contest” where the students are expected to use different machine learning and deep learning methods for time series forecasting. The course is limited to 20 students per semester and they can decide whether to work alone or form a group with another student.

Predictive Modeling for Pre-Conditioning in Vehicles

Machine Learning approach for hiring demand forecasting in Large Scale Organizations

In the field of human resources management, the ability to forecast hiring demand with precision is critical for optimizing workforce planning and talent acquisition strategies. As organizations become increasingly complex, traditional forecasting methods, such as simple time series models or heuristic approaches, often fall short of capturing the multifaceted nature of hiring dynamics. In large multinational corporations, forecasting hiring demand requires the consideration of various factors, including macroeconomic indicators, organizational structure, and workforce fluctuations. This thesis proposes the development of a sophisticated machine learning workflow to enhance the accuracy and reliability of hiring demand predictions.

EcoScapes: LLM-powered advice for crafting sustainable cities

Climate adaptation is vital for the sustainability and sometimes the mere survival of our urban
areas [1, chapters TS.C.8 and TS.D.1]. However, small cities often struggle with limited personnel
resources and integrating vast amounts of data from multiple sources for a comprehensive analysis
[1, chapter TS.D.1.3]. Moreover, the complexity of the topic can overwhelm administrative staff and
local politicians alike. To overcome these challenges, this thesis proposes a multi-layered system
combining specialized Large Language Models (LLMs), satellite imagery and a knowledge base to aid
in developing effective climate adaptation strategies.
Initially, the system uses provided location information to request relevant satellite imagery, which can
be used by all subsequent components.
The architecture’s modular core encompasses several LLMs and expert systems that examine the
satellite data to offer insights on different climate adaptation aspects. Examples of potential functions
might include, but are not limited to, the identification of heat islands, areas threatened by flooding, or
the assessment of vegetation cover.
In the last step, the system consolidates the findings from the preceding modules to generate a
comprehensive report on the existing situation and recommend potential adaptation strategies.
In order to assess the system’s performance, we will compare the generated outputs with those of
unaltered LLMs and a model inspired by ChatClimate [2].

References

[1] P¨ortner, H.-O., D.C. Roberts, H. Adams. et al. 2022: Technical Summary. [H.-O. P¨ortner, D.C. Roberts,
E.S. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegr´ıa, M. Craig, S. Langsdorf, S. L¨oschke, V. M¨oller,
A. Okem (eds.)]. In: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of
Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change
[H.-O. P¨ortner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegr´ıa, M. Craig, S.
Langsdorf, S. L¨oschke, V. M¨oller, A. Okem, B. Rama (eds.)]. Cambridge University Press, Cambridge,
UK and New York, NY, USA, pp. 37-118, doi:10.1017/9781009325844.002.

[2] Vaghefi, S.A., Stammbach, D., Muccione, V. et al. ChatClimate: Grounding conversational AI in climate
science. Commun Earth Environ 4, 480 (2023). https://doi.org/10.1038/s43247-023-01084-x