Irregular time series (IRTS) are prevalent in real-world applications, where uneven sampling and missing data pose fundamental challenges to deep learning-based feature modeling. Although existing methods attempt to retain timestamp information, they often overlook the structured patterns embedded within the missingness itself, and tend to perform poorly when confronted with class imbalance exacerbated by data incompleteness. Specifically, temporal irregularity hinders the modeling of long-range dependencies and local patterns, while sparse observations limit representational capacity, disproportionately impairing minority classes and leading to severe classification bias. To address these deeply coupled challenges, we propose SPECTRA (Structured Pattern and Enriched Context-aware Temporal Representation Architecture), a unified framework for robust IRTS classification. SPECTRA introduces a frequency-guided observation encoder that reconstructs temporal dependencies in a stable manner, mitigating spectral distortion and information corruption. Complementarily, a missingness pattern encoder explicitly captures the dynamic evolution of missing data and leverages it as a discriminative signal. In addition, a prototype-constrained classification paradigm directly optimizes the geometric structure of the feature space, enhancing intra-class compactness and alleviating generalization bottlenecks caused by class imbalance. Extensive experiments on three public IRTS datasets—P12, P19, and PAM—demonstrate the superior performance of SPECTRA under both missing and imbalanced conditions.

Multimodal sensor data from inertial measurement units (IMUs), including accelerometers, gyroscopes, and inclinometers, encode environmental conditions that are difficult to capture through images or text. In maritime settings, classifying sea states from such sensor streams is important for autonomous navigation but faces two interacting difficulties. First, training relies heavily on synthetic simulation data whose idealized physics diverge from real ocean measurements, creating a domain gap. Second, extreme sea states are rare in both domains, and the resulting class imbalance is compounded by the much larger volume of synthetic samples, which together skew gradient updates away from the scarce but operationally important real-world tail classes. We propose A2P-Net, an end-to-end framework that tackles both problems jointly. An adaptive heterogeneous encoder with decoupled channel–temporal attention maps variable-dimension sensor inputs into a shared latent space. Domain-adversarial training aligns synthetic and real feature distributions in that space, and prototype-based metric learning builds per-class retrieval anchors while an asymmetric weighting scheme up-weights real-domain samples to correct the optimization bias. On two custom sea state datasets that mix real and simulated ship motion recordings, A2P-Net reaches 98.7% and 98.9% F1 on real-only evaluation, outperforming the strongest baseline by 1.1–1.3 percentage points. It also ranks first on 15 of 30 UEA multivariate time-series benchmarks with an average accuracy of 74.2%.

Semi-supervised learning (SSL) has gained significant attention in the domain of sea state estimation (SSE) due to its capacity to alleviate the reliance of deep learning models on extensive labeled datasets. While existing semi-supervised SSE methodologies leveraging pseudo-labeling have achieved promising results, they often overlook the challenges posed by high class imbalance and the prevalence of missing data in ship motion datasets, which restricts their broader applicability. In this article, we propose a novel SSL approach BalanceSSE based on the class-imbalanced ship motion data for SSE. This approach consists of three main modules: 1) the dynamic imputation (DIT); 2) the imbalance temporal-frequency learning (ITFL); and 3) the ClusterProx classifier (CL). The DIT module dynamically imputes incomplete ship motion data by assigning different weights to various dimensions data. The ITFL module employs time-frequency collaborative learning to generate pseudo-labels and integrate an adaptive confidence strategy to select high confidence pseudo-labels. This process is further enhanced by the CL module to produce better estimates. Experimental tests on UCR datasets and ship motion datasets demonstrate that BalanceSSE outperforms state-of-the-art methods. Ablation studies highlight the critical role of each module in BalanceSSE.

As wind energy grows in importance, blade icing threatens turbine efficiency and safety. Existing approaches based solely on convolutional neural networks (CNNs) for Euclidean-space feature extraction struggle with complex dynamics and domain shifts. To address this, we propose the domain-generalizable network for icing turbines via mixed Euclidean and Hilbert representations (DGMEHIT), which runs a CNN deep module and a Hilbert statistical module (HSM) in parallel to extract complementary local sequential and global statistical features from Euclidean-space and Hilbert space. Raw features are mapped into a high-dimensional Hilbert space to enhance global statistical separability and discriminate superficially similar signals. A channel–temporal mixer module further models dynamic dependencies by fusing multichannel and time-domain information. In addition, a maximum mean discrepancy loss is incorporated into the HSM to improve feature consistency across domains, enabling the learning of domain-generalizable representations and enhancing the model’s adaptability to varying geographical and climatic conditions. Experiments on ten public time-series datasets and one real-world icing dataset show DGMEHIT surpasses state-of-the-art methods, improving F1 by 6.4% and MCC by 12.7%, with generalization tests and online evaluations confirming practical robustness.

Online anomaly detection is a critical technique in intelligent industrial systems. Concept drift and missing data that arise during the transmission of real-time data streams can severely impact the performance of anomaly detection. Therefore, ensuring the accuracy of online anomaly detection and adaptability to concept drift in the presence of missing values is a significant challenge. In this paper, we propose an online anomaly detection model that integrates an online imputation module based on Generative Adversarial Networks (GANs) to efficiently impute missing data. Additionally, following the dynamic model pool strategy, the model dynamically selects and adjusts the optimal models in the pool to effectively respond to changes in data distribution, ensuring detection performance under complex conditions. We evaluated the model's performance across multiple datasets with concept drift and under varying missing data rates. The results demonstrate that the proposed model not only adapts flexibly to rapidly changing data streams but also exhibits enhanced robustness in the presence of missing data.

Autonomous ships utilize automation systems to achieve unmanned navigation, driving innovation in maritime transportation. However, sea conditions, influenced by dynamic factors such as wave height, wind speed, and ocean currents, present a challenge in accurately assessing these conditions. Traditional classification models often assume accurate labels, but noisy labels are prevalent in real-world applications. Existing methods, such as noise sample filtering or loss function adjustment, have limited applicability and poor generalization when dealing with complex sea condition data. To address this issue, this study proposes an end-to-end neural network model. The model's feature extraction module uses deep representation learning to capture latent patterns in the data, and a loss function is designed to mitigate the impact of outliers. The integration of these components allows the model to perform accurate classification even in the presence of noisy labels. Extensive experiments on public and sea condition datasets validate the effectiveness of this approach, demonstrating that the model exhibits strong generalization capabilities and holds great promise for practical applications.

Wind turbine blade icing poses a critical challenge to wind power generation in high-latitude regions, necessitating innovative solutions for reliable icing detection. To address this challenge while leveraging the abundance of unlabeled data and preserving data privacy, this study proposes a novel federated semi-supervised prototype learning framework, FedIce. By integrating prototype learning and federated learning, FedIce extracts representative class prototypes at the client level and performs global model updates through federated averaging, significantly enhancing robustness against data heterogeneity. Additionally, it incorporates an advanced separation margin strategy to effectively alleviate the adverse effects of class imbalance. Comprehensive experiments using real-world datasets from 20 wind turbines across two wind farms demonstrate that FedIce outperforms existing methods, achieving a remarkable 95.58% improvement in the$mF_{\beta }$metric and a 33.25% enhancement in the mBA metric compared to FedMatch.

The adoption of autonomous vessels has been accelerated by the flourishing maritime economy and more stringent shipping regulations. Accurate sea state estimation (SSE) is of paramount importance for their safe operation. Traditional SSE methods face several limitations, including subjectivity in manual observation, high radar costs, and the insufficient timeliness of satellite data. In contrast, SSE methods based on ship motion data, particularly deep learning models, offer advantages in capturing complex nonlinear relationships. However, challenges persist, such as imbalanced sea state data, suboptimal performance under extreme conditions, and the complexity of ship motion data, which hampers effective feature extraction. To address these challenges, this paper proposes a novel deep learning model that integrates a dynamic attention mechanism with adaptive selective kernel modules and multi-scale feature fusion to capture spatiotemporal dependencies. Additionally, an enhanced prototype classifier, utilizing cosine similarity and dynamic prototype updating, is introduced to mitigate data imbalance and improve model robustness in extreme conditions. To evaluate the performance of the proposed model, we conducted experiments on 30 benchmark datasets for multivariate time series classification, as well as two sea state datasets. The comparison results demonstrate that our model outperforms most baseline methods in general multivariate time series classification tasks and also surpasses state-of-the-art methods for SSE and class imbalance learning. Furthermore, the experimental results show that the proposed model is robust to data noise and missing values.

Missing data and noisy labeling are common problems in time series analysis. The traditional approach to deal with missing data is to separate interpolation and classification, which is not interactive and provides unsatisfactory performance. While advanced methods can learn features from missing information, feature representation is limited due to the accumulation of interpolation errors. For noisy label interference, a robust loss function is a simpler and more general solution for robust learning. This study proposes an end-to-end neural network that unifies data interpolation and feature learning within a single framework. The focus is placed on extracting useful information from incomplete time series data, and for the computation of classification loss, a robustness loss function is used which effectively reduces the impact of noisy labels. The model is evaluated on 20 univariate time series from the UCR archive after noise processing. The results show that the model outperforms state-of-the-art methods in classifying incomplete time series under noisy labels, especially at high missing rates with high noise rates.

Online anomaly detection is a critical area in data analysis, particularly for handling dynamic data streams and addressing the challenge of concept drift. While current methods for online anomaly detection have achieved significant breakthroughs, creating a system that can continuously and effectively learn in scenarios with missing data remains a formidable challenge. In this paper, we introduce an autoencoder-based online deep anomaly detection model that addresses both missing data and concept drift. The model features a lightweight module specifically designed for efficient missing value processing. Additionally, it incorporates an adaptive model pool to manage the time-varying concept drift commonly observed in dynamic data streams. This flexible and dynamic management mechanism allows the model to adapt to changes in the data stream, maintaining robust anomaly detection performance across various conditions. Empirical validation of our model through ten comparative experiments on high-dimensional datasets affected by concept drift shows that it outperforms existing state-of-the-art methods. These results underscore the effectiveness and practicality of our approach.

Wind energy is crucial for sustainable systems but faces reduced productivity due to blade icing. Current detection methods are either costly or heavily reliant on domain-specific knowledge. Data-driven methods show promising performance but encounter challenges such as extracting multi-scale features for blade icing detection from noisy sensor data and addressing the imbalance between icing and non-icing states. To overcome these challenges, we propose an innovative data-driven approach named Multilevel Signal Decomposition Layer-Specific Residual Network (MSD-LRN) for blade icing detection. Our model first employs wavelet decomposition to extract multi-scale features from noisy sensor data and then uses heterogeneous structures to learn the hidden knowledge at each scale. This heterogeneous structure is designed to address the issue of inconsistent information across different scales in wavelet transforms, where information successively decreases. We address data imbalance using resampling techniques. Our approach is validated on three blade icing datasets with varying imbalance ratios, achieving F1 scores of 89.60%, 83.02%, and 78.18%, surpassing existing baselines. Additionally, we introduce random Gaussian noise to test the model's ability to learn robust features from noisy data through wavelet decomposition.

Sea State Estimation (SSE) is essential for Internet of Things (IoT)-enabled autonomous ships, which rely on favorable sea conditions for safe and efficient navigation. Traditional methods, such as wave buoys and radars, are costly, less accurate, and lack real-time capability. Model-driven methods, based on physical models of ship dynamics, are impractical due to wave randomness. Data-driven methods are limited by the data imbalance problem, as some sea states are more frequent and observable than others. To overcome these challenges, we propose a novel data-driven approach for SSE based on ship motion data. Our approach consists of three main components: a data preprocessing module, a parallel convolution feature extractor, and a theoretical-ensured distance-based classifier. The data preprocessing module aims to enhance the data quality and reduce sensor noise. The parallel convolution feature extractor uses a kernel-varying convolutional structure to capture distinctive features. The distance-based classifier learns representative prototypes for each sea state and assigns a sample to the nearest prototype based on a distance metric. The efficiency of our model is validated through experiments on two SSE datasets and the UEA archive, encompassing thirty multivariate time series classification tasks. The results reveal the generalizability and robustness of our approach.