Abstract Time series classification (TSC) is a crucial task across various domains, and its performance heavily depends on the quality of input representations. Among various representations, the recurrence plot (RP) effectively captures topological recurrence, the unique property of time series data. However, conventional convolutional neural networks (CNNs) cannot fully exploit this property since they treat the RP as grid-like data. In this study, we propose RP-GCN, a novel approach that uses a graph convolutional network (GCN) to exploit topological recurrence inherent in the RP, thereby improving TSC performance. Our method transforms a multivariate time series into graphs where state matrices act as node feature matrices and RPs serve as adjacency matrices, enabling graph convolution to utilize recurrence relationships. We evaluated RP-GCN on 35 benchmark multivariate time series classification datasets and demonstrated superior accuracy and efficient inference time compared to existing methods.

Accurate precipitation estimation is essential in agricultural production, water resource management, and flood forecasting. However, high-precision precipitation data remain very hard to obtain due to the complex spatio-temporal distribution of precipitation. Most existing methods considering spatio-temporal correlations in precipitation rely on a convolutional neural network for spatial feature extraction. However, these methods are less efficient in capturing global spatial features due to the local receptive fields of convolutional operators. In this study, we designed a Self-LSTM cell structure capable of effectively capturing temporal and global spatial features. Based on this, a self-attention precipitation fusion model (SAPFM) is proposed. The results demonstrate that SAPFM outperforms basic models and the original precipitation products. SAPFM improves by 28.8% and 21.8% on the Kling-Gupta efficiency (KGE) and Correlation Coefficient (CC) compared to the best-performing precipitation product (GsMap), respectively. Additionally, SAPFM reduces the Root Mean Square Error (RMSE) by 12.5%.

Abstract Predictive Maintenance (PdM) emerges as a critical task of Industry 4.0, driving operational efficiency, minimizing downtime, and reducing maintenance costs. However, real-world industrial environments present unsolved challenges, especially in predicting simultaneous and correlated faults under evolving conditions. Traditional batch-based and deep learning approaches for simultaneous fault prediction often fall short due to their assumptions of static data distributions and high computational demands, making them unsuitable for dynamic, resource-constrained systems. In response, we propose OEMLHAT (Online Ensemble of Multi-Label Hoeffding Adaptive Trees), a novel model tailored for real-time, multi-label fault prediction in non-stationary industrial settings. OEMLHAT introduces a scalable online ensemble architecture that integrates online bagging, dynamic feature subspacing, and adaptive output weighting. This design allows it to efficiently handle concept drift, high-dimensional input spaces, and label sparsity, key bottlenecks in existing PdM solutions. Experimental results on three public multi-label PdM case studies demonstrate substantial improvements in predictive performance of OEMLHAT over previous batch-based and online proposals for multi-label classification, particularly with an average improvement in micro-averaged F1-score of 18.49% over the second most-accurate batch-based proposal and of 8.56% in the case of the second best online model. By addressing a critical gap in online multi-label learning for PdM, this work provides a robust and interpretable solution for next-generation industrial monitoring systems for fault detection, particularly for rare and concurrent failures.