Anomaly detection in time series: a comprehensive evaluation
Detecting anomalous subsequences in time series data is an important task in areas
ranging from manufacturing processes over finance applications to health care monitoring …
ranging from manufacturing processes over finance applications to health care monitoring …
Tranad: Deep transformer networks for anomaly detection in multivariate time series data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …
importance for modern industrial applications. However, building a system that is able to …
Revisiting time series outlier detection: Definitions and benchmarks
Time series outlier detection has been extensively studied with many advanced algorithms
proposed in the past decade. Despite these efforts, very few studies have investigated how …
proposed in the past decade. Despite these efforts, very few studies have investigated how …
Benchmarking a new paradigm: Experimental analysis and characterization of a real processing-in-memory system
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …
Do deep neural networks contribute to multivariate time series anomaly detection?
Anomaly detection in time series is a complex task that has been widely studied. In recent
years, the ability of unsupervised anomaly detection algorithms has received much attention …
years, the ability of unsupervised anomaly detection algorithms has received much attention …
Benchmarking a new paradigm: An experimental analysis of a real processing-in-memory architecture
Many modern workloads, such as neural networks, databases, and graph processing, are
fundamentally memory-bound. For such workloads, the data movement between main …
fundamentally memory-bound. For such workloads, the data movement between main …
NATSA: a near-data processing accelerator for time series analysis
Time series analysis is a key technique for extracting and predicting events in domains as
diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and …
diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and …
ClaSP: parameter-free time series segmentation
The study of natural and human-made processes often results in long sequences of
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …
temporally-ordered values, aka time series (TS). Such processes often consist of multiple …
Matrix profile XXIV: scaling time series anomaly detection to trillions of datapoints and ultra-fast arriving data streams
Time series anomaly detection remains one of the most active areas of research in data
mining. In spite of the dozens of creative solutions proposed for this problem, recent …
mining. In spite of the dozens of creative solutions proposed for this problem, recent …
Dive into time-series anomaly detection: A decade review
Recent advances in data collection technology, accompanied by the ever-rising volume and
velocity of streaming data, underscore the vital need for time series analytics. In this regard …
velocity of streaming data, underscore the vital need for time series analytics. In this regard …