Your task is to develop a Python script capable of detecting anomalies in a continuous data stream. This stream, simulating real-time sequences of floating-point numbers, could represent various metrics such as financial transactions or system metrics. Your focus will be on identifying unusual patterns, such as exceptionally high values or deviations from the norm.
- Algorithm Selection: Identify and implement a suitable algorithm for anomaly detection, capable of adapting to concept drift and seasonal variations. ✅
- Data Stream Simulation: Design a function to emulate a data stream, incorporating regular patterns, seasonal elements, and random noise. ✅
- Anomaly Detection: Develop a real-time mechanism to accurately flag anomalies as the data is streamed. ✅
- Optimization: Ensure the algorithm is optimized for both speed and efficiency. ✅
- Visualization: Create a straightforward real-time visualization tool to display both the data stream and any detected anomalies. ✅
- The project must be implemented using Python 3.x.
- Your code should be thoroughly documented, with comments to explain key sections.
- Include a concise explanation of your chosen algorithm and its effectiveness.
- Ensure robust error handling and data validation.
- Limit the use of external libraries. If necessary, include a requirements.txt file.