Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications. Despite the large number of anomaly detection methods proposed in the literature, recent benchmark studies have shown that no single detector performs best across highly he...
Christos Panourgias, Roberto Stanzione, Adrien Petralia et al.· 0 citations
Approximate filtered vector search (FVS), a core operation in many data management tasks that combine structured data with vector embeddings, exhibits increased complexity due to the characteristics of filtering predicates. Each predicate is defined by selectivity (i.e., the fraction of vectors that satisfy the predica...
Manos Chatzakis, Duo Lu, Helena Caminal et al.· 0 citations
Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable representation learning, classification still typically requires fitting a task-specific classifier on each target dataset, while individual ch...
Jun Fang, Shi-Feng Xie, Rui-Chu Cai et al.· 0 citations
This work analyzes TSFM post-training methods based on their locus of intervention in the prediction pipeline, yielding five categories: parameter adaptation, context augmentation, model composition, output processing and uncertainty control, and compression and specialization.
Tabby, a long context probabilistic time series foundation model, is released together with a complete and open recipe of how it was built, which achieves competitive zero-shot forecasting performance on GIFT-Eval and the out-of-distribution TIME benchmark.
Shi-Feng Xie, Bahaeddine Abdessalem, Ze-Hao Xiao et al.· 1 citation
LoaDiff is introduced, a diffusion-based generative model for year-long, sub-hourly smart-meter load curves that generates realistic and diverse load profiles, achieves a favorable trade-off between generation quality and limited evidence of memorization, and preserves information useful for downstream energy applicati...
M. Baranova, Adrien Petralia, Étienne Le Naour et al.· 0 citations
This work formalizes Agentic ER as a decision-theoretic problem, proposes a reference architecture, identifies core research challenges, and outlines new evaluation dimensions tailored to agentic behavior.
George Papadakis, Panos Korovesis, Manolis Koubarakis et al.· arXiv.org· 0 citations
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