Anomaly Detection With Supervised/Unsupervised Machine Learning.

1. Anomaly detection in time series data involves identifying unexpected patterns that do not conform to the expected behavior.
2. Time series data has an inherent temporal ordering, which makes anomaly detection more challenging but also provides additional contextual information.
3. Both supervised and unsupervised machine learning approaches has been applied to time series anomaly detection.

🛠 Skills

Tslab - Anomaly Detection Python - Scikit-Learn, Matplotlib,Pandas,Numpy

Lessons Learned

1. Understanding Temporal Dependencies: I gained a deeper understanding of how time series data's inherent temporal structure influences model selection and feature engineering.
2. Feature Engineering Importance: I learned the critical role of feature engineering, such as creating lag features and handling seasonality, in improving model performance.
3. Threshold Setting for Unsupervised Methods: I realized the importance of setting appropriate thresholds for anomaly scores to balance false positives and false negatives.