This Python script implements an efficient algorithm for detecting anomalies in a continuous data stream. The data stream is simulated as a real-time sequence of floating-point numbers representing various metrics. Anomalies to detect include unusually high values, breaks in patterns, or deviations from the expected norm.
- Algorithm Selection: Utilize the Isolation Forest algorithm for anomaly detection, capable of handling concept drift and seasonality.
- Data Stream Simulation: Simulate a data stream with a pattern, seasonal component, and noise.
- Anomaly Detection: Implement real-time anomaly detection as the data stream is processed.
- Optimization: Optimize the algorithm for speed and efficiency.
- Visualization: Provide a real-time visualization of the data stream and detected anomalies.
- Included in
requirements.txtfile
- Install dependencies:
pip install -r requirements.txt - Run the script:
python anomaly_detection_script.py
anomaly_detection_script.py: Main Python script implementing the anomaly detection.README.md: Project documentation.requirements.txt: List of external libraries and their versions.
- Shubhradeep Das