awesome-tsad
github.com/yanexr/awesome-tsad ↗List of time series anomaly detection resources, including methods, datasets, benchmarks, libraries, frameworks, and papers.
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me libraries & frameworks resources from awesome-tsad"
Installation instructions →What's inside
Libraries & Frameworks
- aeon
aeon is a scikit-learn compatible toolkit for learning from time series and provides a variety of anomaly detection algorithms, including MERLIN, LOF, STOMP, and more.
- Darts
A library for forecasting and anomaly detection on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks.
- DeepOD
A library for deep learning-based tabular and time-series anomaly detection. Includes models such as TranAD, DeepIsolationForestTS, DeepSADTS, DevNetTS, PReNetTS and more.
- EasyTSAD
A framework for running and evaluating your TSAD algorithm including several built-in methods such as SubLOF, SAND, Donut, EncDecAD, Anomaly Transformer and more.
- Flow Forecast
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
- Kats
A time series analysis toolkit for understanding key statistics and characteristics, detecting regressions and anomalies, and forecasting future trends.
Survey & Review Papers
- Anomaly detection in time series: a comprehensive evaluation
2022
- Anomaly detection in univariate time-series: A survey on the state-of-the-art
2020
- A Unifying Review of Deep and Shallow Anomaly Detection
2020
- Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
2021
- Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
2023
- Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines
2021
Showing a sample of 23 resources. View the full list on GitHub →