99 citations · 522 across the 74 of their papers we have counts for
24 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
Adrien Petralia, Paul Boniol, Philippe Charpentier +1
Improving smart grid system management is crucial in the fight against climate change, and enabling consumers to play an active role in this effort is a significant challenge for e…
NILMFormer: Non-Intrusive Load Monitoring that Accounts for Non-Stationarity
Adrien Petralia, Philippe Charpentier, Youssef Kadhi +1
Millions of smart meters have been deployed worldwide, collecting the total power consumed by individual households. Based on these data, electricity suppliers offer their clients…
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning
Xiaotian Lin, Yanlin Qi, Yizhang Zhu +4
Instruction tuning has emerged as a critical paradigm for improving the capabilities and alignment of large language models (LLMs). However, existing iterative model-aware data sel…
Graphint: Graph-based Time Series Clustering Visualisation Tool
Paul Boniol, Donato Tiano, Angela Bonifati +1
With the exponential growth of time series data across diverse domains, there is a pressing need for effective analysis tools. Time series clustering is important for identifying p…
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly Detection
Paul Boniol, Ashwin K. Krishna, Marine Bruel +7
Anomaly detection (AD) is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains…
-Graph: A Graph Embedding for Interpretable Time Series Clustering
Paul Boniol, Donato Tiano, Angela Bonifati +1
Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, of…
Dive into Time-Series Anomaly Detection: A Decade Review
Paul Boniol, Qinghua Liu, Mingyi Huang +2
Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this re…
User-friendly Foundation Model Adapters for Multivariate Time Series Classification
Vasilii Feofanov, Romain Ilbert, Malik Tiomoko +2
Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory. This paper addresses the challenge of making these models…
SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov +4
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multiv…
Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting
Romain Ilbert, Thai V. Hoang, Zonghua Zhang +1
Balancing the trade-off between accuracy and robustness is a long-standing challenge in time series forecasting. While most of existing robust algorithms have achieved certain subo…
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series
Paul Boniol, Themis Palpanas
Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches proposed so far in the literature have…
dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification
Paul Boniol, Mohammed Meftah, Emmanuel Remy +1
Data series classification is an important and challenging problem in data science. Explaining the classification decisions by finding the discriminant parts of the input that led…