Transformers have been actively studied for time-series forecasting in recent years. While
often showing promising results in various scenarios, traditional Transformers are not …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

The fast adaptation capability of deep neural networks in non-stationary environments is
critical for online time series forecasting. Successful solutions require handling changes to …

We present a novel time series anomaly detection method that achieves excellent detection
accuracy while offering a superior level of explainability. Our proposed method, TimeVQVAE …

Solar activity has significant impacts on human activities and health. One most commonly
used measure of solar activity is the sunspot number. This paper compares three important …

The accumulation of time-series data and the absence of labels make time-series Anomaly
Detection (AD) a self-supervised deep learning task. Single-normality-assumption-based …

Deep learning models have revolutionized research fields like computer vision and natural
language processing by outperforming traditional models in multiple tasks. However, the …

This demo paper presents a design and implementation of a system AnomalyKiTS for
detecting anomalies from time series data for the purpose of offering a broad range of …

Organizations leverage anomaly and changepoint detection algorithms to detect changes in
user behavior or service availability and performance. Many off-the-shelf detection …

Anomaly detection is essential for preventing hazardous outcomes for safety-critical
applications like autonomous driving. Given their safety-criticality, these applications benefit …