GitHub

An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection

TSB-UAD is a new open, end-to-end benchmark suite to ease the evaluation of univariate time-series anomaly detection methods. Overall, TSB-UAD contains 12686 time series with labeled anomalies ...
IEEE

Multivariate Time Series Anomaly Detection Based on Time-Frequency Combination

Abstract: Time series anomaly detection is a core task for ensuring the stability of modern industrial, financial, and operational systems. However, most existing methods model data in the time domain ...
GitHub

Time Series Analysis with LSTM, ARIMA, Prophet, and Anomaly Detection

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...
Scientific Research Publishing

Mou, X., Wang, R. and Li, Y. (2025) RoCA: Robust Contrastive One-Class Time Series Anomaly ...

ABSTRACT: Time series anomaly detection is important in fields such as industrial control, but faces challenges such as data distribution drifting over time, diverse normal patterns, and training data ...
Scientific Research Publishing

Time Series Anomaly Detection Based on the Combination of Trend Feature Discrimination and ...

Time series anomaly detection is important in fields such as industrial control, but faces challenges such as data distribution drifting over time, diverse normal patterns, and training data ...
IEEE

Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies—A Review

Abstract: Detecting anomalies in time-series data is essential for identifying outliers and issuing early warnings of system failures in applications, such as industrial control systems (ICSs), ...