arXiv subjects

Axel Karlsson

Publications and source records attributed to Axel Karlsson.

3 recordsLinked to original sources

arXiv2025

Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift

Time series foundation models (TSFMs) have shown strong results on public benchmarks, prompting comparisons to a "BERT moment" for time series. Their effectiveness in industrial settings, however, remains uncertain. We examine why TSFMs often struggle to generalize and highlight spectral shift (a mismatch between the dominant frequency components in downstream tasks and those represented during pretraining) as a key factor. We present evidence from an industrial-scale player engagement prediction task in mobile gaming, where TSFMs underperform domain-adapted baselines. To isolate the mechanism, we design controlled synthetic experiments contrasting signals with seen versus unseen frequency bands, observing systematic degradation under spectral mismatch. These findings position frequency awareness as critical for robust TSFM deployment and motivate new pretraining and evaluation protocols that explicitly account for spectral diversity.

cs.LG↗

arXiv2024

Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework

Existing evaluations of tabular synthesis models rely primarily on low-order statistics and downstream task performance, leaving multivariate causal relationships that go beyond pairwise correlations largely unmeasured. We argue that a systematic evaluation on high-order structural information is a crucial first step in addressing this issue in tabular data synthesis. In this paper, we present high-order structural causal information as a natural form of prior knowledge and introduce a benchmark framework to evaluate tabular synthesis models. This framework allows us to generate benchmark datasets through a flexible range of data generation processes, allowing for the training of tabular synthesis models using these datasets for further evaluation. We propose multiple benchmark tasks, high-order metrics, and causal inference tasks as downstream tasks for evaluating the quality of synthetic data generated by the trained models. Our experiments demonstrate the effectiveness of the benchmark framework in evaluating the model's ability to capture high-order structural causal information. Furthermore, our benchmarking results provide an initial assessment of state-of-the-art tabular synthesis models. These results reveal significant gaps between ideal and actual performance and highlight how baseline methods differ. We position the framework as a controlled diagnostic benchmark for causal fidelity, complementing existing low-order and downstream evaluations. We open source the benchmark framework, including both code and data along with documentation, to support further research in this area.

cs.LG↗

arXiv2022

Energy-Efficiency Evaluation of OpenMP Loop Transformations and Runtime Constructs

OpenMP is the de facto API for parallel programming in HPC applications. These programs are often computed in data centers, where energy consumption is a major issue. Whereas previous work has focused almost entirely on performance, we here analyse aspects of OpenMP from an energy consumption perspective. This analysis is accomplished by executing novel microbenchmarks and common benchmark suites on data center nodes and measuring the energy consumption. Three main aspects are analysed: directive-generated loop tiling and unrolling, parallel for loops and explicit tasking, and the policy of handling blocked threads. For loop tiling and unrolling, we find that tiling can yield significant energy savings for some, mostly unoptimised programs, while directive-generated unrolling provides very minor improvement in the best case and degenerates performance majorly in the worst case. For the second aspect, we find that parallel for loops yield better results than explicit tasking loops in cases where both can be used. This becomes more prominent with more fine-grained workloads. For the third, we find that significant energy savings can be made by not descheduling waiting threads, but instead having them spin, at the cost of a higher power consumption. We also analyse how the choice of compiler affects the above questions by compiling programs with each of ICC, Clang and GCC, and find that while neither is strictly better than the others, they can produce very different results for the same compiled programs. As a final step, we combine the findings of all results and suggest novel compiler directives as well as general recommendations on how to reduce energy consumption in OpenMP programs.

cs.DC↗