6 papers
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
Qian Sun, Yong-Ming Tian, Jia-Wei Huang +2
Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization. Modern multivariate…
1D-Bench: A Benchmark for Iterative UI Code Generation with Visual Feedback in Real-World
Qiao Xu, Yipeng Yu, Chengxiao Feng +1
Design-to-code translates high-fidelity UI designs into executable front-end implementations, but progress remains hard to compare due to inconsistent datasets, toolchains, and eva…
A Comparative Study on How Data Normalization Affects Zero-Shot Generalization in Time Series Foundation Models
Ihab Ahmed, Denis Krompaß, Cheng Feng +1
We investigate input normalization methods for Time-Series Foundation Models (TSFMs). While normalization is well-studied in dataset-specific time-series models, it remains overloo…
TSFM in-context learning for time-series classification of bearing-health status
Michel Tokic, Slobodan Djukanović, Anja von Beuningen +1
We introduce a classification method based on in-context learning using time-series foundation models (TSFMs). We demonstrate how data not included in the TSFM training can be clas…
Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction
Cheng Feng, Long Huang, Denis Krompass
We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M h…
PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation
Cheng Feng
While new and effective methods for anomaly detection are frequently introduced, many studies prioritize the detection task without considering the need for explainability. Yet, in…