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 users of a mobile communication network. In addition, we develop a simple non - GNN baseline model using recently proposed EdgeBank method. Our analysis shows that the ROLAND temporal GNN outperforms the baseline model in most cases, whereas the other three GNNs perform on average worse than the baseline. The results show that GNN based approaches hold promise in the analysis of temporal social networks through mobile connectivity data. However, due to the relatively small performance margin between ROLAND and the baseline model, further research is required on specialized GNN architectures for temporal social network analysis.

Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations

Astronomical time series from large-scale surveys like LSST are often irregularly sampled and incomplete, posing challenges for classification and anomaly detection. We introduce a new framework based on Neural Stochastic Delay Differential Equations (Neural SDDEs) that combines stochastic modeling with neural networks to capture delayed temporal dynamics and handle irregular observations. Our approach integrates a delay-aware neural architecture, a numerical solver for SDDEs, and mechanisms to robustly learn from noisy, sparse sequences. Experiments on irregularly sampled astronomical data demonstrate strong classification accuracy and effective detection of novel astrophysical events, even with partial labels. This work highlights Neural SDDEs as a principled and practical tool for time series analysis under observational constraints.

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data

Modern time series analysis demands frameworks that are flexible, efficient, and extensible. However, many existing Python libraries exhibit limitations in modularity and in their native support for irregular, multi-source, or sparse data. We introduce pyFAST, a research-oriented PyTorch framework that explicitly decouples data processing from model computation, fostering a cleaner separation of concerns and facilitating rapid experimentation. Its data engine is engineered for complex scenarios, supporting multi-source loading, protein sequence handling, efficient sequence- and patch-level padding, dynamic normalization, and mask-based modeling for both imputation and forecasting. pyFAST integrates LLM-inspired architectures for the alignment-free fusion of sparse data sources and offers native sparse metrics, specialized loss functions, and flexible exogenous data fusion. Training utilities include batch-based streaming aggregation for evaluation and device synergy to maximize computational efficiency. A comprehensive suite of classical and deep learning models (Linears, CNNs, RNNs, Transformers, and GNNs) is provided within a modular architecture that encourages extension. Released under the MIT license at GitHub, pyFAST provides a compact yet powerful platform for advancing time series research and applications.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the importance of accurate and scalable diagnostic systems. Electrocardiogram (ECG) analysis is central to detecting cardiac abnormalities, yet challenges such as noise, class imbalance, and dataset heterogeneity limit current methods. To address these issues, we propose FoundationalECGNet, a foundational framework for automated ECG classification. The model integrates a dual-stage denoising by Morlet and Daubechies wavelets transformation, Convolutional Block Attention Module (CBAM), Graph Attention Networks (GAT), and Time Series Transformers (TST) to jointly capture spatial and temporal dependencies in multi-channel ECG signals. FoundationalECGNet first distinguishes between Normal and Abnormal ECG signals, and then classifies the Abnormal signals into one of five cardiac conditions: Arrhythmias, Conduction Disorders, Myocardial Infarction, QT Abnormalities, or Hypertrophy. Across multiple datasets, the model achieves a 99% F1-score for Normal vs. Abnormal classification and shows state-of-the-art performance in multi-class disease detection, including a 99% F1-score for Conduction Disorders and Hypertrophy, as well as a 98.9% F1-score for Arrhythmias. Additionally, the model provides risk level estimations to facilitate clinical decision-making. In conclusion, FoundationalECGNet represents a scalable, interpretable, and generalizable solution for automated ECG analysis, with the potential to improve diagnostic precision and patient outcomes in healthcare settings. We'll share the code after acceptance.

MAESTRO: Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization

Timely and robust influenza incidence forecasting is critical for public health decision-making. To address this, we present MAESTRO, a Multi-modal Adaptive Ensemble for Spectro-Temporal Robust Optimization. MAESTRO achieves robustness by adaptively fusing multi-modal inputs-including surveillance, web search trends, and meteorological data-and leveraging a comprehensive spectro-temporal architecture. The model first decomposes time series into seasonal and trend components. These are then processed through a hybrid feature enhancement pipeline combining Transformer-based encoders, a Mamba state-space model for long-range dependencies, multi-scale temporal convolutions, and a frequency-domain analysis module. A cross-channel attention mechanism further integrates information across the different data modalities. Finally, a temporal projection head performs sequence-to-sequence forecasting, with an optional estimator to quantify prediction uncertainty. Evaluated on over 11 years of Hong Kong influenza data (excluding the COVID-19 period), MAESTRO shows strong competitive performance, demonstrating a superior model fit and relative accuracy, achieving a state-of-the-art R-square of 0.956. Extensive ablations confirm the significant contributions of both multi-modal fusion and the spectro-temporal components. Our modular and reproducible pipeline is made publicly available to facilitate deployment and extension to other regions and pathogens.Our publicly available pipeline presents a powerful, unified framework, demonstrating the critical synergy of advanced spectro-temporal modeling and multi-modal data fusion for robust epidemiological forecasting.

A Correction for the Paper "Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis"

The symplectic geometry mode decomposition (SGMD) is a powerful method for decomposing time series, which is based on the diagonal averaging principle (DAP) inherited from the singular spectrum analysis (SSA). Although the authors of SGMD method generalized the form of the trajectory matrix in SSA, the DAP is not updated simultaneously. In this work, we pointed out the limitations of the SGMD method and fixed the bugs with the pulling back theorem for computing the given component of time series from the corresponding component of trajectory matrix.

Explainable AI (XAI) for Arrhythmia detection from electrocardiograms

Advancements in deep learning have enabled highly accurate arrhythmia detection from electrocardiogram (ECG) signals, but limited interpretability remains a barrier to clinical adoption. This study investigates the application of Explainable AI (XAI) techniques specifically adapted for time-series ECG analysis. Using the MIT-BIH arrhythmia dataset, a convolutional neural network-based model was developed for arrhythmia classification, with R-peak-based segmentation via the Pan-Tompkins algorithm. To increase the dataset size and to reduce class imbalance, an additional 12-lead ECG dataset was incorporated. A user needs assessment was carried out to identify what kind of explanation would be preferred by medical professionals. Medical professionals indicated a preference for saliency map-based explanations over counterfactual visualisations, citing clearer correspondence with ECG interpretation workflows. Four SHapley Additive exPlanations (SHAP)-based approaches: permutation importance, KernelSHAP, gradient-based methods, and Deep Learning Important FeaTures (DeepLIFT), were implemented and compared. The model achieved 98.3% validation accuracy on MIT-BIH but showed performance degradation on the combined dataset, underscoring dataset variability challenges. Permutation importance and KernelSHAP produced cluttered visual outputs, while gradient-based and DeepLIFT methods highlighted waveform regions consistent with clinical reasoning, but with variability across samples. Findings emphasize the need for domain-specific XAI adaptations in ECG analysis and highlight saliency mapping as a more clinically intuitive approach

Safe Gap-based Planning in Dynamic Settings

This chapter extends the family of perception-informed gap-based local planners to dynamic environments. Existing perception-informed local planners that operate in dynamic environments often rely on emergent or empirical robustness for collision avoidance as opposed to performing formal analysis of dynamic obstacles. This proposed planner, dynamic gap, explicitly addresses dynamic obstacles through several steps in the planning pipeline. First, polar regions of free space known as gaps are tracked and their dynamics are estimated in order to understand how the local environment evolves over time. Then, at planning time, gaps are propagated into the future through novel gap propagation algorithms to understand what regions are feasible for passage. Lastly, pursuit guidance theory is leveraged to generate local trajectories that are provably collision-free under ideal conditions. Additionally, obstacle-centric ungap processing is performed in situations where no gaps exist to robustify the overall planning framework. A set of gap-based planners are benchmarked against a series of classical and learned motion planners in dynamic environments, and dynamic gap is shown to outperform all other baselines in all environments. Furthermore, dynamic gap is deployed on a TurtleBot2 platform in several real-world experiments to validate collision avoidance behaviors.

Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks

Echo State Networks (ESNs) are a particular type of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) framework, popular for their fast and efficient learning. However, traditional ESNs often struggle with long-term information processing. In this paper, we introduce a novel class of deep untrained RNNs based on temporal residual connections, called Deep Residual Echo State Networks (DeepResESNs). We show that leveraging a hierarchy of untrained residual recurrent layers significantly boosts memory capacity and long-term temporal modeling. For the temporal residual connections, we consider different orthogonal configurations, including randomly generated and fixed-structure configurations, and we study their effect on network dynamics. A thorough mathematical analysis outlines necessary and sufficient conditions to ensure stable dynamics within DeepResESN. Our experiments on a variety of time series tasks showcase the advantages of the proposed approach over traditional shallow and deep RC.

ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents

Motion sensor time-series are central to human activity recognition (HAR), with applications in health, sports, and smart devices. However, existing methods are trained for fixed activity sets and require costly retraining when new behaviours or sensor setups appear. Recent attempts to use large language models (LLMs) for HAR, typically by converting signals into text or images, suffer from limited accuracy and lack verifiable interpretability. We propose ZARA, the first agent-based framework for zero-shot, explainable HAR directly from raw motion time-series. ZARA integrates an automatically derived pair-wise feature knowledge base that captures discriminative statistics for every activity pair, a multi-sensor retrieval module that surfaces relevant evidence, and a hierarchical agent pipeline that guides the LLM to iteratively select features, draw on this evidence, and produce both activity predictions and natural-language explanations. ZARA enables flexible and interpretable HAR without any fine-tuning or task-specific classifiers. Extensive experiments on 8 HAR benchmarks show that ZARA achieves SOTA zero-shot performance, delivering clear reasoning while exceeding the strongest baselines by 2.53x in macro F1. Ablation studies further confirm the necessity of each module, marking ZARA as a promising step toward trustworthy, plug-and-play motion time-series analysis. Our codes are available at https://github.com/zechenli03/ZARA.