{"id":15783616,"url":"https://github.com/deepmancer/timeseries-anomaly-detection","last_synced_at":"2026-02-22T09:35:25.308Z","repository":{"id":163432965,"uuid":"537750098","full_name":"deepmancer/timeseries-anomaly-detection","owner":"deepmancer","description":"Analysis of Classical Machine Learning Algorithms for Anomaly Detection in Time Series Data","archived":false,"fork":false,"pushed_at":"2024-08-16T13:23:49.000Z","size":416,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-10-11T20:02:03.485Z","etag":null,"topics":["autoencoder","isolation-forest","local-outlier-factor","machine-learning-algorithms","python","rolling-statistics","scikit-learn","scikitlearn-machine-learning","step-by-step","step-by-step-guide","time-series","time-series-anomaly-detection","z-score"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deepmancer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-09-17T08:47:53.000Z","updated_at":"2024-08-27T21:22:41.000Z","dependencies_parsed_at":null,"dependency_job_id":"d276152c-ce16-4fde-9e78-16ba3d67966c","html_url":"https://github.com/deepmancer/timeseries-anomaly-detection","commit_stats":null,"previous_names":["alirezaheidari-cs/anomaly-detection","deepmancer/anomaly-detection","deepmancer/timeseries-anomaly-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/deepmancer/timeseries-anomaly-detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmancer%2Ftimeseries-anomaly-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmancer%2Ftimeseries-anomaly-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmancer%2Ftimeseries-anomaly-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmancer%2Ftimeseries-anomaly-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deepmancer","download_url":"https://codeload.github.com/deepmancer/timeseries-anomaly-detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepmancer%2Ftimeseries-anomaly-detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281425557,"owners_count":26499031,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-28T02:00:06.022Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["autoencoder","isolation-forest","local-outlier-factor","machine-learning-algorithms","python","rolling-statistics","scikit-learn","scikitlearn-machine-learning","step-by-step","step-by-step-guide","time-series","time-series-anomaly-detection","z-score"],"created_at":"2024-10-04T20:00:21.102Z","updated_at":"2025-10-28T10:40:50.663Z","avatar_url":"https://github.com/deepmancer.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📈 Time Series Classic Anomaly Detection\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/scikitlearn-F7931E.svg?style=for-the-badge\u0026logo=scikit-learn\u0026logoColor=white\" alt=\"scikit-learn\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Python-3.6+-3776AB.svg?style=for-the-badge\u0026logo=Python\u0026logoColor=white\" alt=\"Python\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Jupyter-Notebook-F37626.svg?style=for-the-badge\u0026logo=Jupyter\u0026logoColor=white\" alt=\"Jupyter\"\u003e\n\u003c/p\u003e\n\n---\n\n## 📖 Overview\n\nThis repository provides an in-depth exploration of time series anomaly detection techniques, utilizing classic machine learning models. The project is implemented in a Jupyter Notebook, which offers an interactive environment to experiment with and understand the methods used for detecting anomalies in time series data.\n\n---\n\n## 📝 Project Description\n\nTime series anomaly detection is a critical task in various domains, from finance to IoT, where identifying unexpected behavior in data is essential for decision-making and security. This project explores classic anomaly detection techniques tailored for time series data, including methods like:\n\n- **Z-Score Analysis**: Detects anomalies based on statistical deviation.\n- **Rolling Statistics**: Uses moving averages and standard deviations to flag anomalies.\n- **Isolation Forest**: A tree-based model that isolates anomalies in high-dimensional data.\n- **Local Outlier Factor (LOF)**: Identifies anomalies based on the local density of data points.\n- **Autoencoders**: Neural networks trained to reconstruct input data, with anomalies detected as poorly reconstructed instances.\n\n### Key Features:\n\n- **Step-by-Step Implementation**: Each method is implemented from scratch, with explanations provided for the underlying concepts.\n- **Interactive Exploration**: The Jupyter Notebook format allows for interactive experimentation, making it easy to adjust parameters and observe the effects on anomaly detection.\n- **Visualizations**: Detailed plots and graphs are included to visualize the data and highlight detected anomalies.\n\n---\n\n## 🛠️ Prerequisites\n\nEnsure that you have the following dependencies installed to run the Jupyter Notebook:\n\n- **Python 3.6+**\n- **Jupyter Notebook**\n- **Pandas**\n- **NumPy**\n- **Scikit-learn**\n- **Matplotlib**\n- **Seaborn**\n\nYou can install the required libraries via pip:\n\n```bash\npip install jupyter pandas numpy scikit-learn matplotlib seaborn\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmancer%2Ftimeseries-anomaly-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeepmancer%2Ftimeseries-anomaly-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeepmancer%2Ftimeseries-anomaly-detection/lists"}