Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
A python library for user-friendly forecasting and anomaly detection on time series.
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
List of tools & datasets for anomaly detection on time-series data.
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
Find big moving stocks before they move using machine learning and anomaly detection
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
A collection of official Agent Skills for querying, diagnosing, and managing VictoriaMetrics products (metrics, logs, traces, and alerts).
Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
A python library for user-friendly forecasting and anomaly detection on time series.
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
List of tools & datasets for anomaly detection on time-series data.
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
Find big moving stocks before they move using machine learning and anomaly detection
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
A collection of official Agent Skills for querying, diagnosing, and managing VictoriaMetrics products (metrics, logs, traces, and alerts).
Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
A python library for user-friendly forecasting and anomaly detection on time series.
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
List of tools & datasets for anomaly detection on time-series data.
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
Find big moving stocks before they move using machine learning and anomaly detection
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
A collection of official Agent Skills for querying, diagnosing, and managing VictoriaMetrics products (metrics, logs, traces, and alerts).
Annuaire de skills
Chaque recommandation reste clairement reliée à son dépôt, son audit et son chemin d’installation.
A Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
A python library for user-friendly forecasting and anomaly detection on time series.
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
List of tools & datasets for anomaly detection on time-series data.
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
Find big moving stocks before they move using machine learning and anomaly detection
Anomaly detection using LoOP: Local Outlier Probabilities, a local density based outlier detection method providing an outlier score in the range of [0,1].
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)
A collection of official Agent Skills for querying, diagnosing, and managing VictoriaMetrics products (metrics, logs, traces, and alerts).