The rapid expansion of the Internet of Things (IoT) has created a growing demand for large-scale sensor deployment. However, the high cost of physical sensors limits the scalability and coverage of sensor networks, making fine-grained sensing difficult. Inductive Spatio-Temporal Kriging (ISK) addresses this challenge by introducing virtual sensors that infer measurements from physical sensors, typically using graph neural networks (GNNs) to model their relationships. Despite its promise, current ISK methods often rely on standard message-passing and generic architectures that fail to effectively capture spatio-temporal features or represent virtual nodes accurately. Additionally, existing graph construction techniques suffer from sparse and noisy connections, further hindering performance. To address these limitations, we propose DarkFarseer, a novel ISK framework with three key innovations. First, the Style-enhanced Temporal-Spatial architecture adopts a temporal-then-spatial processing scheme with a temporal style transfer mechanism to enhance virtual node representations. Second, Regional-semantic Contrastive Learning improves representation learning by aligning virtual nodes with regional component patterns. Third, the Similarity-Based Graph Denoising Strategy mitigates the influence of noisy edges by leveraging temporal similarity and regional structure. Extensive experiments on real-world datasets demonstrate that DarkFarseer significantly outperforms state-of-the-art ISK methods.

Missing data is ubiquitous phenomenon in the time series community, significantly challenging forecasting due to incomplete ground truth and sparse data. Most previous Multi-variate Time Series Forecasting with Missing Values (MTSFMV) approaches usually assume static missing patterns, neglecting the dynamic changes over time and space, leading to suboptimal forecasting results. To tackle these challenges, we propose novel STMPANets, which are capable of perceiving time-varying spatiotemporal missing patterns to refine the forecasting sequences. Specifically, we decompose the series into seasonal trend components, allowing STMPANets to highlight inherent sequence properties and adapt to missing patterns. We then propose a Multi-granularity Conditional Partial TCN (MGCPT) to regulate the imputation rate of missing values over time, modeling temporal correlation. Additionally, we design an Adaptive Dynamic GCN (ADGCN) to capture spatial dependencies by perceiving dynamic missing patterns. Extensive experiments demonstrate that STMPANets outperform state-of-the-art models.

Missing values are prevalent in multivariate time series forecasting, especially when certain variables are entirely missing, posing a significant challenge to traditional methods. Two-stage models that combine imputation and forecasting methods are prone to introduce bias and lead to error accumulation. In contrast, existing end-to-end forecasting models with missing data recovery components fail to ensure data consistency when missing variables. Diffusion models, known for their robust generative capabilities, prefer to generate consistent results based on the available observations. Consequently, we propose a novel end-to-end denoising diffusion model named DiffMissing for multivariate time series forecasting with variable missing. DiffMissing employs a contextual conditional encoder to extract local and global contextual information from the observed variables, assisting the denoising network in generating missing variables that align with the true distribution. Meanwhile, the dynamic interaction between observed and missing variables is modeled through a carefully designed adaptive sparse attention, ensuring consistency among variables. Extensive experimental results show that DiffMissing outperforms existing methods in prediction performance on multiple real-world datasets, especially in the high missing rate scenario of 90%, where the MAE is improved by an average of 9.90% over the best baseline.

Neonatal seizures are a prevalent clinical manifestation of neurological disorders and can potentially impact the neurodevelopment of the infant’s brain. Accurate and timely detection of neonatal epilepsy is crucial for early diagnosis and treatment. However, because of the complexity of newborn brains and signal instability, current seizure detection methods often produce false positives and negatives. In this work, we propose an automated scheme for detecting neonatal seizures, namely MSHENet. Through a multi-scale hierarchical modeling approach, the model constructs deep learning models at various levels, including the time dimension, channel dimension, and local details, to differentiate between normal brain electrical activity and epileptic seizures. Experiments conducted on a comprehensive real public neonatal electroencephalogram (EEG) dataset demonstrate that our method achieves higher accuracy in detection and a lower rate of false positives and negatives.