Driven by massive amounts of data and important advances in computational resources,
new deep learning systems have achieved outstanding results in a large spectrum of …

Time-series data are widespread in real-world industrial scenarios. To recover and infer
missing information in real-world applications, the problem of time-series prediction has …

Performance diagnosis systems are defined as detecting abnormal performance
phenomena and play a crucial role in cloud applications. An effective performance …

Skeletal motion plays a vital role in human activity recognition as either an independent data
source or a complement. The robustness of skeleton-based activity recognizers has been …

Convolutional neural network has achieved remarkable success, and has excellent local
feature extraction ability. Similarly, Transformer has been developed markedly in recent …

In recent years, researchers proposed a variety of deep learning models for wind power
forecasting. These models predict the wind power generation of wind farms or entire regions …

Deep neural networks (DNNs) for time series classification have potential security concerns
due to their vulnerability to adversarial attacks. Previous work that perturbs time series …

This work studies the threats of adversarial attack on multivariate probabilistic forecasting
models and viable defense mechanisms. Our studies discover a new attack pattern that …

Time series anomaly detection is extensively studied in statistics, economics, and computer
science. Over the years, numerous methods have been proposed for time series anomaly …

Deep learning-based time series models are being extensively utilized in engineering and
manufacturing industries for process control and optimization, asset monitoring, diagnostic …