Anomaly Detection in Time Series

[alo7lika]

Deep Learning Simplified Repository

🔴 Project Title: Anomaly Detection in Time Series Using LSTM Networks

🔴 Aim: To develop an effective model for detecting anomalies in time series data, leveraging LSTM networks for improved accuracy and reliability.

🔴 Dataset: Synthetic Dataset

🔴 Approach: Implement 3-4 algorithms for anomaly detection, such as LSTM, Facebook Prophet Classification, and Isolation Forest. Conduct exploratory data analysis (EDA) to understand the data distribution and characteristics, and compare model performances using accuracy scores to identify the best-fitting model.

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📍 Follow the Guidelines to Contribute in the Project:

• Create a separate folder named as the Project Title.

• Inside that folder, include:

  • Images - For required visualizations.
  • Dataset - For dataset storage or source information.
  • Model - For the developed machine learning models.
  • requirements.txt - List of required packages/libraries.

• In the Model folder, provide a comprehensive README.md with visualizations and conclusions.

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🔴🟡 Points to Note:

• Issues will be assigned on a first-come, first-served basis. 1 Issue == 1 PR.
• Ensure the "Issue Title" and "PR Title" match, including the issue number.
• Follow Contributing Guidelines & Code of Conduct.

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✅ To be Mentioned while taking the issue:

• Full name: Alolika Bhowmik
• GitHub Profile Link: https://github.com/alo7lika
• Email ID: [email protected]
• Participant ID (if applicable): [Your Participant ID]
• Approach for this Project: Implementing LSTM networks for time series anomaly detection and comparing multiple algorithms.
• What is your participant role? GSSOC Contributor

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[alo7lika]

Assign me the task under GSSOC and HACKTOBERFEST @abhisheks008