Anomaly Detection and Evaluating 3W_Dataset

[SirGankalot]

User story

1. As a Developer
2. I need to use our current anomaly detection approaches on the 3W Dataset
3. So that we can determin anomalies inside it and showcase our anomaly detection functionality

Acceptance criteria

• The 3W dataset is evaluated on with our current anomaly detection approaches.
• Use all approaches from our anomaly detection
• All evaluation metrics are computed and logged for each model.
• The best-performing approaches is clearly identified and saved for deployment.
• All evaluation steps are documented and version-controlled.
• All codes are committed correctly and pushed into the repository.

Definition of done (DoD)

• Acceptance criteria are met.
• Work are pushed to the Github repository.
• Create a branch for each backlog items (coding)
• A pull request is created for each related branch.
• The work products in the pull requests are reviewed.
• The corresponding branches are merged and closed.
• The bill of materials section of the planning documents is updated.
• The software architecture should be updated based on features changes.
• Work needs to be documented in the corresponding wiki section
• For new features unit test have to be written.
• Update our forked repository with the latest RTDIP release.
• All unit tests must pass successfully in the CI pipeline.
• If the task involves coding, the implementation is integrated into the RTDIP framework and verified to function correctly within it.

DoD general criteria

• Feature has been fully implemented
• Feature has been merged into the mainline
• All acceptance criteria were met
• Product owner approved features
• All tests are passing
• Developers agreed to release