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The stable operation of Industrial Control Systems (ICS) is critical to industrial production. However, with the advancement of industrialization and informatization, ICS face increasing security threats, particularly from cyber-attacks. As a ...

With the global population on the rise, the frequency and severity of emergency events like fires and traffic accidents are becoming more frequent and severe. Attending to these emergencies demands valuable and limited resources, such as ...

To enhance the robustness of intrusion detection classifiers, we propose a Time Series-based Adversarial Attack Framework (TSAF) targeting the temporal characteristics of network traffic. Initially, adversarial samples are generated using the ...

One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an ...

• CARLA is a novel contrastive learning-based time series anomaly detection framework.
• CARLA uses anomaly injection to create negative samples for contrastive learning.
• CARLA’s effectiveness is validated through extensive and robust ...

Infectious diseases occur when pathogens from other individuals or animals infect a person, causing harm to both individuals and society. Outbreaks of such diseases can pose a significant threat to human health. However, early detection and tracking of ...

Solving the unsupervised domain adaptation (UDA) task in time series is of great significance for practical applications, such as human activity recognition and machine fault diagnosis. Compared to UDA for computer vision, UDA in time series is ...

• The proposed Rainforest can fully capture and transfer the temporal dependencies.
• Fine-tuning the target-specific classifier aids in adapting to the target domain.
• Experiments on four time series datasets demonstrate the ...

Time series anomaly detection is critical in various domains, including stock markets, network traffic monitoring, and industrial systems, as it identifies deviations from expected patterns in data, enabling real-time analysis and timely responses ...

Time series event detectors are evaluated mainly by standard classification metrics, focusing solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring ...

• The SoftED Metrics incorporates temporal tolerance in event detection evaluation.
• It is inspired by fuzzy sets.
• It maintain consistency with hard metrics (Precision, Recall, F1).
• A new general protocol is introduced, inspired ...

For modern industrial applications, accurately detecting and diagnosing anomalies in multivariate time series data is essential. Despite such need, most state-of-the-art methods often prioritize detection performance over model interpretability. ...

We propose an adversarial automatic data augmentation with hard sample suppression by incorrect class likelihood for time series data. Automatic data augmentation (ADA) is a practical framework when the number of training data is small. In ...

Predictions of the Air Quality Index (AQI) can provide information on air quality, aiding individuals in personal protection and environmental conservation, and facilitating policymakers in implementing measures to control air pollution. In the ...

We find that local similarity is an essential factor for data augmentation in deep learning tasks concerning time series data, the applications of which are prevalent in various domains such as smart healthcare, intelligent transportation, smart ...

• Controlled augmentation intensity dramatically improves model performance.
• Local similarity epitomizes pioneering advancements in time series augmentation.
• Precise modulation of local similarity ensures optimal performance during ...

Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can greatly benefit from advances in the field, as state-of-the-art ...

Over the past decades, Dynamic Time Warping (DTW) and its variants have been widely adopted as the most effective similarity measures for time series. Nevertheless, they suffer from high computational complexity, thereby limiting their ...

As Transformer-based models have achieved impressive performance across various time series tasks, Long-Term Series Forecasting (LTSF) has garnered extensive attention in recent years. The intricate complexity of the Attention mechanism leads to ...

Efficient energy management relies heavily on accurate load forecasting, particularly in the face of increasing energy demands and the imperative for sustainable operations. However, the presence of anomalies in historical data poses a significant ...

Forecasting trends in the financial market is a classic and challenging problem that attracts economists’ and computer scientists’ attention. This research area, characterized by its dynamic, chaotic, and nonlinear nature, is further complicated ...

Time series classification is an increasingly attractive field with the appearance of new problems in an expanding digitalized world. Most of the proposals in the state-of-the-art have focused just on improving the results’ performance, leaving ...

• PLAYTIME incorporates temporal information into fuzzy environments.
• PLAYTIME combines fuzzy- and time-features to improve time series classification.
• PLAYTIME improves interpretability and performance by using a tree-based ...

Time series analysis has been widely employed in various domains, including finance, healthcare, meteorology, and economics. This approach is crucial in extracting patterns, discerning trends, and forecasting future data points. Traditional ...

Forecasting stock prices is difficult because of the many unknowns and diverse factors that affect the financial market. Using time series data, the study attempts to assess how well the ARIMA (Auto Regressive Integrated Moving Average) model ...