Tootfinder

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 1437 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Transition of car-based human-mobility in the pandemic era: Data insight from a cross-border region in Europe
Sujit Kumar Sikder, Jyotirmaya Ijaradar, Hao Li, Hichem Omrani
https://arxiv.org/abs/2509.05166

Transition of car-based human-mobility in the pandemic era: Data insight from a cross-border region in Europe
Many transport authorities are collecting and publishing almost real-time road traffic data to meet the growing trend of massive open data, a vital resource for foresight decision support systems considering deep data insights. Using such a traffic count dataset, we explored the spatio-temporal transitions in the cross-country road traffic volumes in the context of modelling behavioural transitions in car-based human mobility. We developed a reproducible workflow for computing multi-dimensional…

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
Ines Akaichi, Giorgos Flouris, Irini Fundulaki, Sabrina Kirrane
https://arxiv.org/abs/2510.04652

Modeling and Managing Temporal Obligations in GUCON Using SPARQL-star and RDF-star
In the digital age, data frequently crosses organizational and jurisdictional boundaries, making effective governance essential. Usage control policies have emerged as a key paradigm for regulating data usage, safeguarding privacy, protecting intellectual property, and ensuring compliance with regulations. A central mechanism for usage control is the handling of obligations, which arise as a side effect of using and sharing data. Effective monitoring of obligations requires capturing usage trac…

Edit-Based Flow Matching for Temporal Point Processes
David L\"udke, Marten Lienen, Marcel Kollovieh, Stephan G\"unnemann
https://arxiv.org/abs/2510.06050 https://

Edit-Based Flow Matching for Temporal Point Processes
Temporal point processes (TPPs) are a fundamental tool for modeling event sequences in continuous time, but most existing approaches rely on autoregressive parameterizations that are limited by their sequential sampling. Recent non-autoregressive, diffusion-style models mitigate these issues by jointly interpolating between noise and data through event insertions and deletions in a discrete Markov chain. In this work, we generalize this perspective and introduce an Edit Flow process for TPPs th…

Implicit Updates for Average-Reward Temporal Difference Learning
Hwanwoo Kim, Dongkyu Derek Cho, Eric Laber
https://arxiv.org/abs/2510.06149 https://arxiv.…

Implicit Updates for Average-Reward Temporal Difference Learning
Temporal difference (TD) learning is a cornerstone of reinforcement learning. In the average-reward setting, standard TD($λ$) is highly sensitive to the choice of step-size and thus requires careful tuning to maintain numerical stability. We introduce average-reward implicit TD($λ$), which employs an implicit fixed point update to provide data-adaptive stabilization while preserving the per iteration computational complexity of standard average-reward TD($λ$). In contrast to prior finite-tim…

Robust Spatio-Temporal Distributional Regression
Tomotaka Momozaki, Shonosuke Sugasawa, Tomoyuki Nakagawa, Hiroko Kato Solvang, Sam Subbey
https://arxiv.org/abs/2508.05041 https…

Robust Spatio-Temporal Distributional Regression
Motivated by investigating spatio-temporal patterns of the distribution of continuous variables, we consider describing the conditional distribution function of the response variable incorporating spatio-temporal components given predictors. In many applications, continuous variables are observed only as threshold-categorized data due to measurement constraints. For instance, ecological measurements often categorize sizes into intervals rather than recording exact values due to practical limita…

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
Austin Feng, Andreas Varvarigos, Ioannis Panitsas, Daniela Fernandez, Jinbiao Wei, Yuwei Guo, Jialin Chen, Ali Maatouk, Leandros Tassiulas, Rex Ying
https://arxiv.org/abs/2510.06063

TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as weather, observability data are zero-inflated, highly stochastic, and exhibit minimal temporal structure. Despite their importance, observability datasets are underrepresented in public benchmarks due to proprietary restrictions. Existing datasets are often anonymized and normalized, removing scale information and limi…

Integrating Weather and Land Cover Data into Geospatial Impact Evaluations
Elinor Benami, Mike Cecil, Anna Josephson, Gina Maskell, Jeffrey D. Michler
https://arxiv.org/abs/2510.05108

Integrating Weather and Land Cover Data into Geospatial Impact Evaluations
Integrating gridded weather and earth observation data into impact evaluations holds great promise. It allows researchers to capture environmental context, external shocks, and even to measure outcomes (e.g., land cover change, agricultural production) that surveys might miss due to spatial or temporal data collection constraints. However, with great power comes great responsibility: the increasing ease of extracting time series from these datasets belies potentially complex geospatial and meas…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Roberto Neglia, Andrea Cini, Michael M. Bronstein, Filippo Maria Bianchi
https://arxiv.org/abs/2510.05060 https…

ResCP: Reservoir Conformal Prediction for Time Series Forecasting
Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample size is small and often require expensive retraining when the underlying data distribution changes. To overcome these limitations, we propose Reservoir Conformal Prediction (ResCP), a…

TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution
Yifei Jia, Shiyu Cheng, Yu Dong, Guan Li, Dong Tian, Ruixiao Peng, Xuyi Lu, Yu Wang, Wei Yao, Guihua Shan
https://arxiv.org/abs/2509.04834

TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution
Understanding the complex combustion dynamics within scramjet engines is critical for advancing high-speed propulsion technologies. However, the large scale and high dimensionality of simulation-generated temporal flow field data present significant challenges for visual interpretation, feature differentiation, and cross-case comparison. In this paper, we present TemporalFlowViz, a parameter-aware visual analytics workflow and system designed to support expert-driven clustering, visualization, …

When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts
Chengyuan Yao, Yunxuan Tang, Christopher Brooks, Rene F. Kizilcec, Renzhe Yu
https://arxiv.org/abs/2509.01060

When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts
Predictive models are typically trained on historical data to predict future outcomes. While it is commonly assumed that training on more historical data would improve model performance and robustness, data distribution shifts over time may undermine these benefits. This study examines how expanding historical data training windows under covariate shifts (changes in feature distributions) and concept shifts (changes in feature-outcome relationships) affects the performance and algorithmic fairn…

Temporal social network modeling of mobile connectivity data with graph neural networks
Joel Jaskari, Chandreyee Roy, Fumiko Ogushi, Mikko Saukkoriipi, Jaakko Sahlsten, Kimmo Kaski
https://arxiv.org/abs/2509.03319

Temporal social network modeling of mobile connectivity data with graph neural networks
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people's mobile connectivity data has not been extensively investigated. In the present study, we investigate four snapshot - based temporal GNNs in predicting the phone call and SMS activity between u…

Predictive inference for time series: why is split conformal effective despite temporal dependence?
Rina Foygel Barber, Ashwin Pananjady
https://arxiv.org/abs/2510.02471 https:/…

Predictive inference for time series: why is split conformal effective despite temporal dependence?
We consider the problem of uncertainty quantification for prediction in a time series: if we use past data to forecast the next time point, can we provide valid prediction intervals around our forecasts? To avoid placing distributional assumptions on the data, in recent years the conformal prediction method has been a popular approach for predictive inference, since it provides distribution-free coverage for any iid or exchangeable data distribution. However, in the time series setting, the str…

Spatial Data Science Symposium 2025 is announced. Check it out, it's hybrid!
http://sdss2025.spatial-data-science.net/index.html

Spatial Data Science Symposium 2025
December 4-5 | Distributed & Online. The symposium aims to bring together researchers from both academia and industry to discuss experiences, insights, methodologies, and applications, taking spatial and temporal knowledge into account while addressing their domain-specific problems.

Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method
Lulu Gong, Shreya Saxena
https://arxiv.org/abs/2510.06091 https://arxiv.o…

Learning Mixtures of Linear Dynamical Systems (MoLDS) via Hybrid Tensor-EM Method
Mixtures of linear dynamical systems (MoLDS) provide a path to model time-series data that exhibit diverse temporal dynamics across trajectories. However, its application remains challenging in complex and noisy settings, limiting its effectiveness for neural data analysis. Tensor-based moment methods can provide global identifiability guarantees for MoLDS, but their performance degrades under noise and complexity. Commonly used expectation-maximization (EM) methods offer flexibility in fitting…

Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
Songyuan Sui, Zihang Xu, Yu-Neng Chuang, Kwei-Herng Lai, Xia Hu
https://arxiv.org/abs/2510.05950

Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and inflexible. Recent reasoning-oriented large language models (LLMs) show promise in understanding temporal patterns, but purely zero-shot usage remains suboptimal. We propose FETA, a multi-agent framework for training-free TSC via exemplar-based in-context reasoning. FETA decomposes a multivariate series into channel-wise subproblems, retrieves a few s…

Emergent Coordination in Multi-Agent Language Models
Christoph Riedl
https://arxiv.org/abs/2510.05174 https://arxiv.org/pdf/2510.05174

Emergent Coordination in Multi-Agent Language Models
When are multi-agent LLM systems merely a collection of individual agents versus an integrated collective with higher-order structure? We introduce an information-theoretic framework to test -- in a purely data-driven way -- whether multi-agent systems show signs of higher-order structure. This information decomposition lets us measure whether dynamical emergence is present in multi-agent LLM systems, localize it, and distinguish spurious temporal coupling from performance-relevant cross-agent …

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 2586 nodes and 3548 edges.
Tags: Economic, Commerce, Weighted, Temporal

PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description
Zihao Zheng, Zeyu Xie, Xuenan Xu, Wen Wu, Chao Zhang, Mengyue Wu
https://arxiv.org/abs/2509.00683

PicoAudio2: Temporal Controllable Text-to-Audio Generation with Natural Language Description
Controllable text-to-audio generation (TTA) has attracted much attention recently. Although existing works can achieve fine-grained controllability based on timestamp information, sound event categories are limited to a fixed set. Moreover, since only simulated data is used for training, the generated audio quality and generalization performance on real data are limited. To tackle this issue, we propose PicoAudio2, improving temporal-controllable TTA via a new data processing pipeline and model…

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Davide Rucci, Emanuele Carlini, Patrizio Dazzi, Hanna Kavalionak, Matteo Mordacchini
https://arxiv.org/abs/2510.00758

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Key graph-based problems play a central role in understanding network topology and uncovering patterns of similarity in homogeneous and temporal data. Such patterns can be revealed by analyzing communities formed by nodes, which in turn can be effectively modeled through temporal $k$-cores. This paper introduces a novel decentralized and incremental algorithm for computing the core decomposition of temporal networks. Decentralized solutions leverage the ability of network nodes to communicate a…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 1619 nodes and 3197 edges.
Tags: Economic, Commerce, Weighted, Temporal

A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
Runshi Tang, Runbing Zheng, Anru R. Zhang, Carey E. Priebe
https://arxiv.org/abs/2509.05221

A functional tensor model for dynamic multilayer networks with common invariant subspaces and the RKHS estimation
Dynamic multilayer networks are frequently used to describe the structure and temporal evolution of multiple relationships among common entities, with applications in fields such as sociology, economics, and neuroscience. However, exploration of analytical methods for these complex data structures remains limited. We propose a functional tensor-based model for dynamic multilayer networks, with the key feature of capturing the shared structure among common vertices across all layers, while simul…

OpenGVL - Benchmarking Visual Temporal Progress for Data Curation
Pawe{\l} Budzianowski, Emilia Wi\'snios, Gracjan G\'oral, Igor Kulakov, Viktor Petrenko, Krzysztof Walas
https://arxiv.org/abs/2509.17321

OpenGVL - Benchmarking Visual Temporal Progress for Data Curation
Data scarcity remains one of the most limiting factors in driving progress in robotics. However, the amount of available robotics data in the wild is growing exponentially, creating new opportunities for large-scale data utilization. Reliable temporal task completion prediction could help automatically annotate and curate this data at scale. The Generative Value Learning (GVL) approach was recently proposed, leveraging the knowledge embedded in vision-language models (VLMs) to predict task prog…

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Mauro Diaz, Luis Sante, Joel Perca, Jo\~ao Victor da Silva, Nivan Ferreira, Jorge Poco
https://arxiv.org/abs/2509.02732

STRive: An association rule-based system for the exploration of spatiotemporal categorical data
Effectively analyzing spatiotemporal data plays a central role in understanding real-world phenomena and informing decision-making. Capturing the interaction between spatial and temporal dimensions also helps explain the underlying structure of the data. However, most datasets do not reveal attribute relationships, requiring additional algorithms to extract meaningful patterns. Existing visualization tools often focus either on attribute relationships or spatiotemporal analysis, but rarely supp…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 5547 nodes and 7447 edges.
Tags: Economic, Commerce, Weighted, Temporal

Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer
Yin Huang, Yongqi Dong, Youhua Tang, Li Li
https://arxiv.org/abs/2509.04362

Parking Availability Prediction via Fusing Multi-Source Data with A Self-Supervised Learning Enhanced Spatio-Temporal Inverted Transformer
The rapid growth of private car ownership has worsened the urban parking predicament, underscoring the need for accurate and effective parking availability prediction to support urban planning and management. To address key limitations in modeling spatio-temporal dependencies and exploiting multi-source data for parking availability prediction, this study proposes a novel approach with SST-iTransformer. The methodology leverages K-means clustering to establish parking cluster zones (PCZs), extr…

Typhoon Path Prediction Using Functional Data Analysis and Clustering-Based Regression
Jimin Kim
https://arxiv.org/abs/2510.02316 https://arxiv.org/pdf/251…

Typhoon Path Prediction Using Functional Data Analysis and Clustering-Based Regression
Accurate prediction of typhoon trajectories is essential for mitigating the impact of these extreme weather events. This study proposes a functional data analysis (FDA) framework for modeling and forecasting typhoon paths using historical trajectory data. Latitude and longitude sequences are represented as smooth functional observations, and a function-on-function regression model is employed to capture the temporal dynamics of typhoon movement. While the baseline model demonstrates reasonable …

CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models
Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski
https://arxiv.org/abs/2509.22360

CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models
Large language models (LLMs) excel at operating at scale by leveraging social media and various data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM's ability to contextualize semantic and normative evolution of language and to capture diachronic variation. To support analysis and training for the latter, we introduce CHRONOBERG, a temporally structured corpus of English book texts spanning 250 years, curat…

Enhanced Hybrid Temporal Computing Using Deterministic Summations for Ultra-Low-Power Accelerators
Sachin Sachdeva, Jincong Lu, Wantong Li, Sheldon X. -D. Tan
https://arxiv.org/abs/2509.22999

Enhanced Hybrid Temporal Computing Using Deterministic Summations for Ultra-Low-Power Accelerators
This paper presents an accuracy-enhanced Hybrid Temporal Computing (E-HTC) framework for ultra-low-power hardware accelerators with deterministic additions. Inspired by the recently proposed HTC architecture, which leverages pulse-rate and temporal data encoding to reduce switching activity and energy consumption but loses accuracy due to its multiplexer (MUX)-based scaled addition, we propose two bitstream addition schemes: (1) an Exact Multiple-input Binary Accumulator (EMBA), which performs …

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

A human EEG dataset for multisensory perception and mental imagery #YOTO (You Only Think Once)

A Human EEG Dataset for Multisensory Perception and Mental Imagery - Scientific Data
The YOTO (You Only Think Once) dataset presents a human electroencephalog- raphy (EEG) resource for exploring multisensory perception and mental imagery. The study enrolled 26 participants who performed tasks involving both unimodal and multimodal stimuli. Researchers collected high-resolution EEG signals at a 1000 Hz sampling rate to capture high-temporal-resolution neural activity related to internal mental representations. The protocol incorporated visual, auditory, and combined cues to in…

from my link log —
LIGO can detect daylight savings time.
https://arxiv.org/abs/2509.11849
saved 2025-09-29 https://dotat.at/:/TJ9D3.html

Can LIGO Detect Daylight Savings Time?
Yes, it can. Catalogs produced by networks of Gravitational-wave interferometers are subject to complicated selection effects, and the gold-standard remains direct measurements of the detection probability through large injection campaigns. I leverage public data products from the LIGO-Virgo-KAGRA Collaborations' 3rd and 4th observing runs to show that there are non-trivial temporal variations within the detection probability that are well-described by a weekly cycle. There are clear differen…

Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors
Xiaolong Deng, Laura Schulz, Martin Schulz
https://arxiv.org/abs/2508.21231 https://

Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors
Quantum computing in supercomputing centers requires robust tools to analyze calibration datasets, predict hardware performance, and optimize operational workflows. This paper presents a data-driven framework for processing calibration metrics. Our model is based on a real calibration quality metrics dataset from our in-house 20-qubit NISQ device and for more than 250 days. We apply detailed data analysis to uncover temporal patterns and cross-metric correlations. Using unsupervised clustering,…

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data
Yi Hu, Zheyuan Cheng
https://arxiv.org/abs/2509.22795 https://

Generative Modeling and Decision Fusion for Unknown Event Detection and Classification Using Synchrophasor Data
Reliable detection and classification of power system events are critical for maintaining grid stability and situational awareness. Existing approaches often depend on limited labeled datasets, which restricts their ability to generalize to rare or unseen disturbances. This paper proposes a novel framework that integrates generative modeling, sliding-window temporal processing, and decision fusion to achieve robust event detection and classification using synchrophasor data. A variational autoe…

Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity
Victor Brabant, Angela Bonifati, R\'emy Cazabet
https://arxiv.org/abs/2510.00741 http…

Discovering Communities in Continuous-Time Temporal Networks by Optimizing L-Modularity
Community detection is a fundamental problem in network analysis, with many applications in various fields. Extending community detection to the temporal setting with exact temporal accuracy, as required by real-world dynamic data, necessitates methods specifically adapted to the temporal nature of interactions. We introduce LAGO, a novel method for uncovering dynamic communities by greedy optimization of Longitudinal Modularity, a specific adaptation of Modularity for continuous-time networks.…

FreeViS: Training-free Video Stylization with Inconsistent References
Jiacong Xu, Yiqun Mei, Ke Zhang, Vishal M. Patel
https://arxiv.org/abs/2510.01686 https://

FreeViS: Training-free Video Stylization with Inconsistent References
Video stylization plays a key role in content creation, but it remains a challenging problem. Naïvely applying image stylization frame-by-frame hurts temporal consistency and reduces style richness. Alternatively, training a dedicated video stylization model typically requires paired video data and is computationally expensive. In this paper, we propose FreeViS, a training-free video stylization framework that generates stylized videos with rich style details and strong temporal coherence. Our…

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
Lingyu Zhang, Guobin Wu, Yan Wang, Pengfei Xu, Jian Liang, Xuan Song, Yunhai Wang
https://arxiv.org/abs/2509.22661

Next Point-of-interest (POI) Recommendation Model Based on Multi-modal Spatio-temporal Context Feature Embedding
The next Point-of-interest (POI) recommendation is mainly based on sequential traffic information to predict the user's next boarding point location. This is a highly regarded and widely applied research task in the field of intelligent transportation, and there have been many research results to date. Traditional POI prediction models primarily rely on short-term traffic sequence information, often neglecting both long-term and short-term preference data, as well as crucial spatiotemporal cont…

HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling
Minjung Park, Gyuyeon Na, Soyoun Kim, Sunyoung Moon, HyeonJeong Cha, Sangmi Chai
https://arxiv.org/abs/2509.03260

HyPV-LEAD: Proactive Early-Warning of Cryptocurrency Anomalies through Data-Driven Structural-Temporal Modeling
Abnormal cryptocurrency transactions - such as mixing services, fraudulent transfers, and pump-and-dump operations -- pose escalating risks to financial integrity but remain notoriously difficult to detect due to class imbalance, temporal volatility, and complex network dependencies. Existing approaches are predominantly model-centric and post hoc, flagging anomalies only after they occur and thus offering limited preventive value. This paper introduces HyPV-LEAD (Hyperbolic Peak-Valley Lead-ti…

Is the Peculiar Galactic Center Transient Swift J174610.4-290018 A Recurrent Nova?
Ziqian Hua, Zhiyuan Li
https://arxiv.org/abs/2509.26446 https://arxiv.or…

Is the Peculiar Galactic Center Transient Swift J174610.4-290018 A Recurrent Nova?
Swift J174610.4-290018, a peculiar transient X-ray source originally discovered by the Swift satellite in February 2024 at the onset of its outburst, recently attracted intentional or coincident follow-up X-ray observations using Swift, NuSTAR and Chandra. We have performed a comprehensive analysis of the relevant X-ray data to investigate the spectral and temporal properties of this source between October 2000 and July 2024. Archival Chandra data reveal a plausible additional outburst in 2005,…

Crosslisted article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers
Giacomo Acciarini, et al.

LiFeChain: Lightweight Blockchain for Secure and Efficient Federated Lifelong Learning in IoT
Handi Chen, Jing Deng, Xiuzhe Wu, Zhihan Jiang, Xinchen Zhang, Xianhao Chen, Edith C. H. Ngai
https://arxiv.org/abs/2509.01434

LiFeChain: Lightweight Blockchain for Secure and Efficient Federated Lifelong Learning in IoT
The expansion of Internet of Things (IoT) devices constantly generates heterogeneous data streams, driving demand for continuous, decentralized intelligence. Federated Lifelong Learning (FLL) provides an ideal solution by incorporating federated and lifelong learning to overcome catastrophic forgetting. The extended lifecycle of FLL in IoT systems increases their vulnerability to persistent attacks, and these risks may be obscured by performance degradation caused by spatial-temporal data heter…

Optimizing the Network Topology of a Linear Reservoir Computer
Sahand Tangerami, Nicholas A. Mecholsky, Francesco Sorrentino
https://arxiv.org/abs/2509.23391 https://

Optimizing the Network Topology of a Linear Reservoir Computer
Machine learning has become a fundamental approach for modeling, prediction, and control, enabling systems to learn from data and perform complex tasks. Reservoir computing is a machine learning tool that leverages high-dimensional dynamical systems to efficiently process temporal data for prediction and observation tasks. Traditionally, the connectivity of a reservoir computer (RC) is generated at random, lacking a principled design. Here, we focus on optimizing the topology of a linear RC to …

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 332 nodes and 18613039 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

General Intuition, which trains AI agents in spatial reasoning using Medal.tv game clips, raised a $133.7M seed led by Khosla Ventures and General Catalyst (Rebecca Bellan/TechCrunch)
https://techcrunch.com/2025/10/16/gene

General Intuition lands $134M seed to teach agents spatial reasoning using video game clips | TechCrunch
Late last year, OpenAI reportedly tried to buy Medal and its vast trove of video game data for $500M. Today, the company spun out a frontier research lab that's using that data to build AI agents that understand how they move through space and time, a concept called spatial-temporal reasoning.

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Mathijs van Noort, Femke Ongenae, Pieter Bonte
https://arxiv.org/abs/2509.10146 https://

On Syntactical Simplification of Temporal Operators in Negation-free MTL
Temporal reasoning in dynamic, data-intensive environments increasingly demands expressive yet tractable logical frameworks. Traditional approaches often rely on negation to express absence or contradiction. In such contexts, Negation-as-Failure is commonly used to infer negative information from the lack of positive evidence. However, open and distributed systems such as IoT networks or the Semantic Web Negation-as-Failure semantics become unreliable due to incomplete and asynchronous data. Th…

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha O\v{z}bot, Igor \v{S}krjanc, Vitomir \v{S}truc
https://arxiv.org/abs/2510.00960 https://

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into i…

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 502 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Data-Driven Bed Occupancy Planning in Intensive Care Units Using $M_t/G_t/\infty$ Queueing Models
Maryam Akbari-Moghaddam, Douglas G. Down, Na Li, Catherine Eastwood, Ayman Abou Mehrem, Alexandra Howlett
https://arxiv.org/abs/2510.02852

Data-Driven Bed Occupancy Planning in Intensive Care Units Using $M_t/G_t/\infty$ Queueing Models
Hospitals struggle to make effective long-term capacity planning decisions for intensive care units (ICUs) under uncertainty in future demand. Admission rates fluctuate over time due to temporal factors, and length of stay (LOS) distributions vary with patient heterogeneity, hospital location, case mix, and clinical practices. Common planning approaches rely on steady-state queueing models or heuristic rules that assume fixed parameters, but these methods often fall short in capturing real-worl…

TACTFL: Temporal Contrastive Training for Multi-modal Federated Learning with Similarity-guided Model Aggregation
Guanxiong Sun, Majid Mirmehdi, Zahraa Abdallah, Raul Santos-Rodriguez, Ian Craddock, Telmo de Menezes e Silva Filho
https://arxiv.org/abs/2509.17532

TACTFL: Temporal Contrastive Training for Multi-modal Federated Learning with Similarity-guided Model Aggregation
Real-world federated learning faces two key challenges: limited access to labelled data and the presence of heterogeneous multi-modal inputs. This paper proposes TACTFL, a unified framework for semi-supervised multi-modal federated learning. TACTFL introduces a modality-agnostic temporal contrastive training scheme that conducts representation learning from unlabelled client data by leveraging temporal alignment across modalities. However, as clients perform self-supervised training on heteroge…

Online Sequential Leveraging Sampling Method for Streaming Autoregressive Time Series with Application to Seismic Data
Rui Xie, T. N. Sriram, Wei Biao Wu, Ping Ma
https://arxiv.org/abs/2509.20698

Online Sequential Leveraging Sampling Method for Streaming Autoregressive Time Series with Application to Seismic Data
Seismic data contain complex temporal information that arrives at high speed and has a large, even potentially unbounded volume. The explosion of temporally correlated streaming data from advanced seismic sensors poses analytical challenges due to its sheer volume and real-time nature. Sampling, or data reduction, is a natural yet powerful tool for handling large streaming data while balancing estimation accuracy and computational cost. Currently, data reduction methods and their statistical pr…

RiverScope: High-Resolution River Masking Dataset
Rangel Daroya, Taylor Rowley, Jonathan Flores, Elisa Friedmann, Fiona Bennitt, Heejin An, Travis Simmons, Marissa Jean Hughes, Camryn L Kluetmeier, Solomon Kica, J. Daniel V\'elez, Sarah E. Esenther, Thomas E. Howard, Yanqi Ye, Audrey Turcotte, Colin Gleason, Subhransu Maji
https://arxi…

RiverScope: High-Resolution River Masking Dataset
Surface water dynamics play a critical role in Earth's climate system, influencing ecosystems, agriculture, disaster resilience, and sustainable development. Yet monitoring rivers and surface water at fine spatial and temporal scales remains challenging -- especially for narrow or sediment-rich rivers that are poorly captured by low-resolution satellite data. To address this, we introduce RiverScope, a high-resolution dataset developed through collaboration between computer science and hydrolog…

ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition
Ali Abouzeid, Bilal Elbouardi, Mohamed Maged, Shady Shehata
https://arxiv.org/abs/2509.01401

ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition
Speech emotion recognition is vital for human-computer interaction, particularly for low-resource languages like Arabic, which face challenges due to limited data and research. We introduce ArabEmoNet, a lightweight architecture designed to overcome these limitations and deliver state-of-the-art performance. Unlike previous systems relying on discrete MFCC features and 1D convolutions, which miss nuanced spectro-temporal patterns, ArabEmoNet uses Mel spectrograms processed through 2D convolutio…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
Miha O\v{z}bot, Igor \v{S}krjanc, Vitomir \v{S}truc
https://arxiv.org/abs/2510.00960 https://

A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into i…

Preference-Based Dynamic Ranking Structure Recognition
Nan Lu, Jian Shi, Xin-Yu Tian
https://arxiv.org/abs/2509.24493 https://arxiv.org/pdf/2509.24493

Preference-Based Dynamic Ranking Structure Recognition
Preference-based data often appear complex and noisy but may conceal underlying homogeneous structures. This paper introduces a novel framework of ranking structure recognition for preference-based data. We first develop an approach to identify dynamic ranking groups by incorporating temporal penalties into a spectral estimation for the celebrated Bradley-Terry model. To detect structural changes, we introduce an innovative objective function and present a practicable algorithm based on dynamic…

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
Xuan Hou, Shuhan Liu, Zhaohui Peng, Yaohui Chu, Yue Zhang, Yining Wang
https://arxiv.org/abs/2508.21340

DLGAN : Time Series Synthesis Based on Dual-Layer Generative Adversarial Networks
Time series synthesis is an effective approach to ensuring the secure circulation of time series data. Existing time series synthesis methods typically perform temporal modeling based on random sequences to generate target sequences, which often struggle to ensure the temporal dependencies in the generated time series. Additionally, directly modeling temporal features on random sequences makes it challenging to accurately capture the feature information of the original time series. To address t…

EventTracer: Fast Path Tracing-based Event Stream Rendering
Zhenyang Li, Xiaoyang Bai, Jinfan Lu, Pengfei Shen, Edmund Y. Lam, Yifan Peng
https://arxiv.org/abs/2508.18071 https:…

EventTracer: Fast Path Tracing-based Event Stream Rendering
Simulating event streams from 3D scenes has become a common practice in event-based vision research, as it meets the demand for large-scale, high temporal frequency data without setting up expensive hardware devices or undertaking extensive data collections. Yet existing methods in this direction typically work with noiseless RGB frames that are costly to render, and therefore they can only achieve a temporal resolution equivalent to 100-300 FPS, far lower than that of real-world event data. In…

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Ahsan Farabi, Muhaiminul Rashid Shad, Israt Khandaker
https://arxiv.org/abs/2510.00572

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD. To address these issues, we propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. The architecture is further optimized using the Squirrel Search Algorithm (SSA), e…

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 4330 nodes and 8711 edges.
Tags: Technological, Communication, Unweighted, Temporal

VisiTrail: A Cognitive Visualization Tool for Time-Series Analysis of Eye Tracking Data from Attention Game
Abdul Rehman, Ilona Heldal, Jerry Chun-Wei Lin
https://arxiv.org/abs/2508.20522

VisiTrail: A Cognitive Visualization Tool for Time-Series Analysis of Eye Tracking Data from Attention Game
Eye Tracking (ET) can help to understand visual attention and cognitive processes in interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses lack comprehensive insights into the temporal dynamics of attention allocation and the relationship between gaze behavior and task perform…

How Far Do Time Series Foundation Models Paint the Landscape of Real-World Benchmarks ?
Lujun Li, Lama Sleem, Yiqun Wang, Yangjie Xu, Niccol\`o Gentile, Radu State
https://arxiv.org/abs/2509.26347

How Far Do Time Series Foundation Models Paint the Landscape of Real-World Benchmarks ?
Recent evaluations of time-series foundation models (TSFMs) have emphasized synthetic benchmarks, leaving real-world generalization less thoroughly examined. This work proposes a novel benchmarking approach that bridges synthetic and realistic data by extracting temporal signals from real-world video using optical flow and curating datasets reflecting everyday temporal dynamics. Building upon this pipeline, we introduce REAL-V-TSFM, a novel dataset designed to capture rich and diverse time seri…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
Yanbo Xu, Yu Wu, Sungjae Park, Zhizhuo Zhou, Shubham Tulsiani
https://arxiv.org/abs/2510.01184 htt…

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or broader distribution than the training distribution. We build on the observation that these models leverage (learned) score functions of noisy data distributions for sampling and show that rescaling these allows one to effectively control a `local' sampling temperature. Notably, this approach does not require any finetuning or alterations to training…

Fuzzy Jump Models for Soft and Hard Clustering of Multivariate Time Series Data
Federico P. Cortese, Antonio Pievatolo, Elisa Maria Alessi
https://arxiv.org/abs/2509.26029 https…

Fuzzy Jump Models for Soft and Hard Clustering of Multivariate Time Series Data
Statistical jump models have been recently introduced to detect persistent regimes by clustering temporal features and discouraging frequent regime changes. However, they are limited to hard clustering and thereby do not account for uncertainty in state assignments. This work presents an extension of the statistical jump model that incorporates uncertainty estimation in cluster membership. Leveraging the similarities between statistical jump models and the fuzzy c-means framework, our fuzzy jum…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Optimal placement of wind farms via quantile constraint learning
Wenxiu Feng, Antonio Alc\'antara, Carlos Ruiz
https://arxiv.org/abs/2510.01093 https://

Optimal placement of wind farms via quantile constraint learning
Wind farm placement arranges the size and the location of multiple wind farms within a given region. The power output is highly related to the wind speed on spatial and temporal levels, which can be modeled by advanced data-driven approaches. To this end, we use a probabilistic neural network as a surrogate that accounts for the spatiotemporal correlations of wind speed. This neural network uses ReLU activation functions so that it can be reformulated as mixed-integer linear set of constraints …

Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models
Xuan Hou, Shuhan Liu, Zhaohui Peng, Yaohui Chu, Yue Zhang, Yining Wang
https://arxiv.org/abs/2508.21330

Stage-Diff: Stage-wise Long-Term Time Series Generation Based on Diffusion Models
Generative models have been successfully used in the field of time series generation. However, when dealing with long-term time series, which span over extended periods and exhibit more complex long-term temporal patterns, the task of generation becomes significantly more challenging. Long-term time series exhibit long-range temporal dependencies, but their data distribution also undergoes gradual changes over time. Finding a balance between these long-term dependencies and the drift in data di…

Beyond Memorization: Reasoning-Driven Synthesis as a Mitigation Strategy Against Benchmark Contamination
Terry Jingchen Zhang, Gopal Dev, Ning Wang, Nicole Ni, Wenyuan Jiang, Yinya Huang, Bernhard Sch\"olkopf, Mrinmaya Sachan, Zhijing Jin
https://arxiv.org/abs/2509.00072

Beyond Memorization: Reasoning-Driven Synthesis as a Mitigation Strategy Against Benchmark Contamination
Capability evaluation of large language models (LLMs) is increasingly shadowed by rising concerns of data contamination that cast doubts on whether static benchmarks measure genuine reasoning or mere memorization. We present an empirical study using an infinitely scalable framework to synthesize research-level QA directly from arXiv papers, harnessing the natural temporal structure of research publications where performance decay after knowledge cutoffs may indicate potential contamination. We …

A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Shalini Dangi, Surya Karthikeya Mullapudi, Chandravardhan Singh Raghaw, Shahid Shafi Dar, Mohammad Zia Ur Rehman, Nagendra Kumar
https://arxiv.org/abs/2509.15966

A multi-temporal multi-spectral attention-augmented deep convolution neural network with contrastive learning for crop yield prediction
Precise yield prediction is essential for agricultural sustainability and food security. However, climate change complicates accurate yield prediction by affecting major factors such as weather conditions, soil fertility, and farm management systems. Advances in technology have played an essential role in overcoming these challenges by leveraging satellite monitoring and data analysis for precise yield estimation. Current methods rely on spatio-temporal data for predicting crop yield, but they …

Generalized Encrypted Traffic Classification Using Inter-Flow Signals
Federica Bianchi, Edoardo Di Paolo, Angelo Spognardi
https://arxiv.org/abs/2508.21558 https://

Generalized Encrypted Traffic Classification Using Inter-Flow Signals
In this paper, we present a novel encrypted traffic classification model that operates directly on raw PCAP data without requiring prior assumptions about traffic type. Unlike existing methods, it is generalizable across multiple classification tasks and leverages inter-flow signals - an innovative representation that captures temporal correlations and packet volume distributions across flows. Experimental results show that our model outperforms well-established methods in nearly every classifi…

Tracking Temporal Evolution of Topological Features in Image Data
Susan Glenn, Jessi Cisewski-Kehe, Jun Zhu, William M Bement
https://arxiv.org/abs/2508.17530 https://

Tracking Temporal Evolution of Topological Features in Image Data
Topological Data Analysis (TDA) can be used to detect and characterize holes in an image, such as zero-dimensional holes (connected components) or one-dimensional holes (loops). However, there is currently no widely accepted statistical framework for modeling spatiotemporal dependence in the evolution of topological features, such as holes, within a time series of images. We propose a hypothesis testing framework to identify statistically significant topological features of images in space and …

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 2948 nodes and 5515 edges.
Tags: Technological, Communication, Unweighted, Temporal

BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting
Shiqiao Zhou, Holger Sch\"oner, Huanbo Lyu, Edouard Fouch\'e, Shuo Wang
https://arxiv.org/abs/2509.00622

BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecasting
Time series forecasting is a long-standing and highly challenging research topic. Recently, driven by the rise of large language models (LLMs), research has increasingly shifted from purely time series methods toward harnessing textual modalities to enhance forecasting performance. However, the vast discrepancy between text and temporal data often leads current multimodal architectures to over-emphasise one modality while neglecting the other, resulting in information loss that harms forecastin…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Seeing Space and Motion: Enhancing Latent Actions with Spatial and Dynamic Awareness for VLA
Zhejia Cai, Yandan Yang, Xinyuan Chang, Shiyi Liang, Ronghan Chen, Feng Xiong, Mu Xu, Ruqi Huang
https://arxiv.org/abs/2509.26251

Seeing Space and Motion: Enhancing Latent Actions with Spatial and Dynamic Awareness for VLA
Latent Action Models (LAMs) enable Vision- Language-Action (VLA) systems to learn semantic action rep- resentations from large-scale unannotated data. Yet, we identify two bottlenecks of LAMs: 1) the commonly adopted end-to-end trained image encoder suffers from poor spatial understanding; 2) LAMs can be fragile when input frames are distant, leading to limited temporal perception. Such factors inevitably hinder stable and clear action modeling. To this end, we propose Farsighted-LAM, a latent …

Forecasting intraday particle number size distribution: A functional time series approach
Han Lin Shang, Israel Martinez Hernandez
https://arxiv.org/abs/2510.01692 https://

Forecasting intraday particle number size distribution: A functional time series approach
Particulate matter data now include various particle sizes, which often manifest as a collection of curves observed sequentially over time. When considering 51 distinct particle sizes, these curves form a high-dimensional functional time series observed over equally spaced and densely sampled grids. While high dimensionality poses statistical challenges due to the curse of dimensionality, it also offers a rich source of information that enables detailed analysis of temporal variation across sho…

Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
Zeqi Ye, Minshuo Chen
https://arxiv.org/abs/2510.02216 https://

Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification
Imputation methods play a critical role in enhancing the quality of practical time-series data, which often suffer from pervasive missing values. Recently, diffusion-based generative imputation methods have demonstrated remarkable success compared to autoregressive and conventional statistical approaches. Despite their empirical success, the theoretical understanding of how well diffusion-based models capture complex spatial and temporal dependencies between the missing values and observed ones…

route_views: Route Views AS graphs (1997-1998)
733 daily network snapshots denoting BGP traffic among autonomous systems (ASs) on the Internet, from the Oregon Route Views Project, spanning 8 November 1997 to 2 January 2000. Data collected by NLANR/MOAT.
This network has 3971 nodes and 7604 edges.
Tags: Technological, Communication, Unweighted, Temporal

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Yukun Yang
https://arxiv.org/abs/2509.26145

LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
Depression is a major global public health challenge and its early identification is crucial. Social media data provides a new perspective for depression detection, but existing methods face limitations such as insufficient accuracy, insufficient utilization of time series features, and high annotation costs. To this end, this study proposes the LMILAtt model, which innovatively integrates Long Short-Term Memory autoencoders and attention mechanisms: firstly, the temporal dynamic features of us…

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Guillem Capellera, Luis Ferraz, Antonio Rubio, Alexandre Alahi, Antonio Agudo
https://arxiv.org/abs/2509.22522

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Generative models often treat continuous data and discrete events as separate processes, creating a gap in modeling complex systems where they interact synchronously. To bridge this gap, we introduce JointDiff, a novel diffusion framework designed to unify these two processes by simultaneously generating continuous spatio-temporal data and synchronous discrete events. We demonstrate its efficacy in the sports domain by simultaneously modeling multi-agent trajectories and key possession events. …

Covariance-Corrected WAIC for Bayesian Sequential Data Models
Safaa K. Kadhem
https://arxiv.org/abs/2509.17980 https://arxiv.org/pdf/2509.17980

Covariance-Corrected WAIC for Bayesian Sequential Data Models
This paper introduces and develops a theoretical extension of the widely applicable information criterion (WAIC), called the Covariance-Corrected WAIC (CC-WAIC), that applied for Bayesian sequential data models. The CC-WAIC accounts for temporal or structural dependence by incorporating the full posterior covariance structure of the log-likelihood contributions, in contrast to the classical WAIC that assumes conditional independence among data. We exploit the limitations of classical WAIC in th…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 13 nodes and 63095 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Sparse Coding Representation of 2-way Data
Boya Ma, Abram Magner, Maxwell McNeil, Petko Bogdanov
https://arxiv.org/abs/2509.10033 https://arxiv.org/pdf/250…

Sparse Coding Representation of 2-way Data
Sparse dictionary coding represents signals as linear combinations of a few dictionary atoms. It has been applied to images, time series, graph signals and multi-way spatio-temporal data by jointly employing temporal and spatial dictionaries. Data-agnostic analytical dictionaries, such as the discrete Fourier transform, wavelets and graph Fourier, have seen wide adoption due to efficient implementations and good practical performance. On the other hand, dictionaries learned from data offer spar…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 4499 nodes and 4891 edges.
Tags: Economic, Commerce, Weighted, Temporal

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Junyi Xie, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique
https://arxiv.org/abs/2510.12076

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anoma…

sp_baboons: Baboons' interactions (2020)
Network of interactions between a group of 20 Guinea baboons living in an enclosure of a Primate Center in France, between June 13th 2019 and July 10th 2019. The data set contains observational and wearable sensors data.
This network has 23 nodes and 3197 edges.
Tags: Social, Animal, Offline, Unweighted, Weighted, Temporal, Metadata

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services
Lian Lian, Yilin Li, Song Han, Renzi Meng, Sibo Wang, Ming Wang
https://arxiv.org/abs/2508.14503

Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services
This study proposes an anomaly detection method based on the Transformer architecture with integrated multiscale feature perception, aiming to address the limitations of temporal modeling and scale-aware feature representation in cloud service environments. The method first employs an improved Transformer module to perform temporal modeling on high-dimensional monitoring data, using a self-attention mechanism to capture long-range dependencies and contextual semantics. Then, a multiscale featur…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 403 nodes and 1417485 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Feature Dynamics as Implicit Data Augmentation: A Depth-Decomposed View on Deep Neural Network Generalization
Tianyu Ruan, Kuo Gai, Shihua Zhang
https://arxiv.org/abs/2509.20334

Feature Dynamics as Implicit Data Augmentation: A Depth-Decomposed View on Deep Neural Network Generalization
Why do deep networks generalize well? In contrast to classical generalization theory, we approach this fundamental question by examining not only inputs and outputs, but the evolution of internal features. Our study suggests a phenomenon of temporal consistency that predictions remain stable when shallow features from earlier checkpoints combine with deeper features from later ones. This stability is not a trivial convergence artifact. It acts as a form of implicit, structured augmentation that…

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 100 nodes and 394247 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer
Xuanhao Mu, G\"okhan Demirel, Yuzhe Zhang, Jianlei Liu, Thorsten Schlachter, Veit Hagenmeyer
https://arxiv.org/abs/2508.10587

Self-Supervised Temporal Super-Resolution of Energy Data using Generative Adversarial Transformer
To bridge the temporal granularity gap in energy network design and operation based on Energy System Models, resampling of time series is required. While conventional upsampling methods are computationally efficient, they often result in significant information loss or increased noise. Advanced models such as time series generation models, Super-Resolution models and imputation models show potential, but also face fundamental challenges. The goal of time series generative models is to learn the…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 4695 nodes and 5118 edges.
Tags: Economic, Commerce, Weighted, Temporal

Pi-Transformer: A Physics-informed Attention Mechanism for Time Series Anomaly Detection
Sepehr Maleki, Negar Pourmoazemi
https://arxiv.org/abs/2509.19985 https://

Pi-Transformer: A Physics-informed Attention Mechanism for Time Series Anomaly Detection
Anomalies in multivariate time series often arise from temporal context and cross-channel coordination rather than isolated outliers. We present Pi-Transformer, a physics-informed transformer with two attention pathways: a data-driven series attention and a smoothly evolving prior attention that encodes temporal invariants such as scale-related self-similarity and phase synchrony. The prior acts as a stable reference that calibrates reconstruction error. During training, we pair a reconstructio…

eu_procurements_alt: EU national procurement networks (2008-2016)
These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.
This network has 2728 nodes and 3204 edges.
Tags: Economic, Commerce, Weighted, Temporal

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 1437 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 668 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 1437 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 668 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

sp_high_school: High school temporal contacts (2013)
These data sets correspond to the contacts and friendship relations between students in a high school in Marseilles, France, in December 2013, as measured through several techniques.
This network has 329 nodes and 502 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata