99 citations · 522 across the 74 of their papers we have counts for
81 papers
Retrieval Over Training: Similarity search-based Model Selection for Time Series Anomaly Detection
Christos Panourgias, Roberto Stanzione, Adrien Petralia +2
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 anom…
TREMOR: Template Matching for Large Seismic Data Collections
Manos Chatzakis, Rodrigo Flores-Allende, Mikael Freire +3
Seismic station networks continuously record the ground velocity at several locations on earth in the form of waveform data series, which seismologists analyze to detect various ki…
VADER: Filtered Vector Search with Declarative Recall
Manos Chatzakis, Duo Lu, Helena Caminal +7
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 t…
Tabby: An Open Pretraining Recipe for Time Series Foundation Models
Shifeng Xie, Bahaeddine Abdessalem, Zehao Xiao +9
In this report, we release Tabby, a long context probabilistic time series foundation model, together with a complete and open recipe of how it was built. Tabby adopts an encoder-o…
LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics
Mariia Baranova, Adrien Petralia, Etienne Le Naour +3
The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances, and demand-response progra…
Time-Decayed Vector Search in the Rhythm of TANGO: Jointly Modeling Semantic Similarity and Temporal Freshness
Jiuqi Wei, Qiyao Luo, Quanqing Xu +2
Vector search typically measures relevance through semantic similarity under a fixed scoring function. However, in a growing range of applications, relevance may evolve over time,…
ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning
Juntao Fang, Shifeng Xie, Ruichu Cai +6
Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support forecasting and transferable rep…
AgenticER: the next frontier in Entity Resolution
George Papadakis, Panos Korovesis, Manolis Koubarakis +1
Entity Resolution (ER) is a fundamental problem in data management, playing a critical role in tasks like data cleaning and knowledge graph construction. The existing ER approaches…
Post-Training in Time Series Foundation Models: A Unifying Framework
Shifeng Xie, Ambroise Odonnat, Zehao Xiao +7
Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deploymen…
Evaluating and Generating Query Workloads for High Dimensional Vector Similarity Search
Matteo Ceccarello, Alexandra Levchenko, Ioana Ileana +1
Similarity search lies at the heart of many modern applications, ranging from databases to deep learning to data series analysis. As such, a vast effort has been invested in develo…
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
Gaspard Berthelier, Mariia Baranova, Andrei-Tiberiu Pantea +4
Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance, often outperforming supervised models in zero-shot settings. Recent TSFM architectures, s…
SEAnet: A Deep Learning Architecture for Data Series Similarity Search
Qitong Wang, Themis Palpanas
A key operation for massive data series collection analysis is similarity search. According to recent studies, SAX-based indexes offer state-of-the-art performance for similarity s…
PDET-LSH: Scalable In-Memory Indexing for High-Dimensional Approximate Nearest Neighbor Search with Quality Guarantees
Jiuqi Wei, Xiaodong Lee, Botao Peng +3
Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search with theoretical guarantees. Traditional LSH-based methods mainly focus on i…
TaCo: Data-adaptive and Query-aware Subspace Collision for High-dimensional Approximate Nearest Neighbor Search
Jiuqi Wei, Zhenyu Liao, Ruoyu Han +3
Approximate Nearest Neighbor Search (ANNS) in high-dimensional Euclidean spaces is a fundamental problem with broad applications. Subspace Collision is a newly proposed ANNS framew…
GALACTIC: Global and Local Agnostic Counterfactuals for Time-series Clustering
Christos Fragkathoulas, Eleni Psaroudaki, Themis Palpanas +1
Time-series clustering is a fundamental tool for pattern discovery, yet existing explainability methods, primarily based on feature attribution or metadata, fail to identify the tr…
DaiSy: A Library for Scalable Data Series Similarity Search
Francesca Del Gaudio, Manos Chatzakis, Gayathiri Ravendirane +2
Exact similarity search over large collections of data series is a fundamental operation in modern applications, yet existing solutions are often fragmented, specialized, or tailor…
ParisKV: Fast and Drift-Robust KV-Cache Retrieval for Long-Context LLMs
Yanlin Qi, Xinhang Chen, Huiqiang Jiang +3
KV-cache retrieval is essential for long-context LLM inference, yet existing methods struggle with distribution drift and high latency at scale. We introduce ParisKV, a drift-robus…
Rethinking Zero-Shot Time Series Classification: From Task-specific Classifiers to In-Context Inference
Juntao Fang, Shifeng Xie, Shengbin Nie +7
The zero-shot evaluation of time series foundation models (TSFMs) for classification typically uses a frozen encoder followed by a task-specific classifier. However, this practice…
MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance
Jens E. d'Hondt, Teun Kortekaas, Odysseas Papapetrou +1
Modern applications frequently collect and analyze temporal data in the form of multivariate time series (MTS) -- time series that contain multiple channels. A common task in this…
MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection
Emmanouil Sylligardos, John Paparrizos, Themis Palpanas +2
Anomaly detection is a fundamental task for time series analytics with important implications for the downstream performance of many applications. Despite increasing academic inter…
Toward Efficient and Scalable Design of In-Memory Graph-Based Vector Search
Ilias Azizi, Karima Echihab, Themis Palpanas +1
Vector data is prevalent across business and scientific applications, and its popularity is growing with the proliferation of learned embeddings. Vector data collections often reac…
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
Shifeng Xie, Vasilii Feofanov, Ambroise Odonnat +7
Time series foundation models (TSFMs) have recently gained significant attention due to their strong zero-shot capabilities and widespread real-world applications. Such models typi…
TiVy: Time Series Visual Summary for Scalable Visualization
Gromit Yeuk-Yin Chan, Luis Gustavo Nonato, Themis Palpanas +2
Visualizing multiple time series presents fundamental tradeoffs between scalability and visual clarity. Time series capture the behavior of many large-scale real-world processes, f…
DeviceScope: An Interactive App to Detect and Localize Appliance Patterns in Electricity Consumption Time Series
Adrien Petralia, Paul Boniol, Philippe Charpentier +1
In recent years, electricity suppliers have installed millions of smart meters worldwide to improve the management of the smart grid system. These meters collect a large amount of…