- Project Owner 👉 : Ye Zhu
- IIT-Kharagpur students are also recommended joining Dr Zhu's related related projects
- Prepared by 🌷 TULIP Lab
Time series anomaly detection is the process of identifying unusual patterns or events in temporal data that deviate from expected or normal behavior. It has numerous applications across diverse domains, including finance, healthcare, security, and manufacturing. However, this task presents several challenges, such as high dimensionality, noise, seasonality, and non-stationarity in the data. This project aims to develop innovative and effective approaches for time series anomaly detection by leveraging statistical models and deep learning techniques. The required skills encompass proficiency in Python programming, a solid foundation in machine learning principles, and a background in statistics.
This project offers an exciting opportunity to explore the frontiers of anomaly detection in industrial environment. You will delve into our lab's established methods and evaluate their potential for new industrial applications. Additionally, you will investigate other cutting-edge, state-of-the-art techniques, leveraging them as a springboard for developing even more sophisticated solutions in the next phase.
📔 NEXUS-S1 (compatible with SIT723)
The related documents are encrypted, and you will receive the password upon the acceptance into stage 1️⃣ of the project.
- TO BE ADDED
📔 NEXUS-S2 (compatible with SIT724)
The related documents are encrypted, and you will receive the password upon the acceptance into the stage 2️⃣ of the project.
- TO BE ADDED
📔 NEXUS-S3 (compatible with Honours)
The related documents are encrypted, and you will receive the password upon the acceptance into the stage 3️⃣ of the project.
- TO BE ADDED