Sascha Kaltenpoth

Marcel Meyer, Sascha Kaltenpoth, Kevin Zalipski et al.

While Time Series Foundation Models (TSFMs) offer transformative capabilities for forecasting, they simultaneously risk triggering a fundamental evaluation crisis. This crisis is driven by information leakage due to overlapping training and test sets across different models, as well as the illegitimate transfer of global patterns to test data. While the ability to learn shared temporal dynamics represents a primary strength of these models, their evaluation on historical archives often permits the exploitation of observed global shocks, which violates the independence required for valid benchmarking. We introduce TS-Arena, a platform that restores the operational integrity of forecasting by treating the genuinely unknown future as the definitive test environment. By implementing a pre-registration mechanism on live data streams, the platform ensures that evaluation targets remain physically non-existent during inference, thereby enforcing a strict global temporal split. This methodology establishes a moving temporal frontier that prevents historical contamination and provides an authentic assessment of model generalization. Initially applied within the energy sector, TS-Arena provides a sustainable infrastructure for comparing foundation models under real-world constraints. A prototype of the platform is available at https://huggingface.co/spaces/DAG-UPB/TS-Arena.

Marcel Meyer, Sascha Kaltenpoth, Kevin Zalipski et al.

Time Series Foundation Models (TSFMs) represent a new paradigm for time series forecasting, offering zero-shot forecasting capabilities without the need for domain-specific pre-training or fine-tuning. However, as with Large Language Models (LLMs), evaluating TSFMs is tricky, as with ever more extensive training sets, it becomes more and more challenging to ensure the integrity of benchmarking data. Our investigation of existing TSFM evaluation highlights multiple challenges, ranging from the representativeness of the benchmark datasets, over the lack of spatiotemporal evaluation, to risks of information leakage due to overlapping and obscure datasets, and the memorization of global patterns caused by external shocks like economic crises or pandemics. Our findings reveal widespread confusion regarding data partitions, risking inflated performance estimates and incorrect transfer of global knowledge to local time series. We argue for the development of robust evaluation methodologies to prevent pitfalls already observed in LLM and classical time series benchmarking, and call upon the research community to design new, principled approaches, such as evaluations on truly out-of-sample future data, to safeguard the integrity of TSFM assessment.

Sascha Kaltenpoth, Oliver Müller

Adopting Large language models (LLMs) in organizations potentially revolutionizes our lives and work. However, they can generate off-topic, discriminating, or harmful content. This AI alignment problem often stems from misspecifications during the LLM adoption, unnoticed by the principal due to the LLM's black-box nature. While various research disciplines investigated AI alignment, they neither address the information asymmetries between organizational adopters and black-box LLM agents nor consider organizational AI adoption processes. Therefore, we propose LLM ATLAS (LLM Agency Theory-Led Alignment Strategy) a conceptual framework grounded in agency (contract) theory, to mitigate alignment problems during organizational LLM adoption. We conduct a conceptual literature analysis using the organizational LLM adoption phases and the agency theory as concepts. Our approach results in (1) providing an extended literature analysis process specific to AI alignment methods during organizational LLM adoption and (2) providing a first LLM alignment problem-solution space.