artlib

Create a real-time anomaly detection system using the Adaptive Resonance Theory (ART) algorithms provided by the 'artlib' Python package. This mini-app will monitor a stream of sensor data from a hypothetical industrial machine and alert the user when anomalies are detected, indicating potential issues with the machine's performance.

Step 1: Set up the environment
- Install Python and necessary libraries including 'artlib'.
- Ensure you have a source of continuous data input, such as simulated sensor data from an IoT device or a CSV file with timestamped readings.

Step 2: Data Preprocessing
- Implement functions to clean and preprocess the incoming sensor data.
- Normalize the data if necessary to ensure accurate ART model training.

Step 3: Model Training and Anomaly Detection
- Utilize 'artlib' to train an ART model on a subset of your preprocessed data.
- Continuously feed the model new data points and use it to detect anomalies based on deviations from learned patterns.

Suggested Features:
- Real-time visualization of the sensor data and anomaly scores.
- Adjustable sensitivity for anomaly detection.
- Historical data storage for analysis of past events.
- Email/SMS alerts when anomalies are detected.

How 'artlib' is utilized:
- Use 'artlib' to instantiate and configure an ART model appropriate for the type of data you're working with (e.g., ART1 for binary data).
- Train the model using a batch of initial data to establish a baseline of normal behavior.
- Continuously update the model with new data points and check for anomalies using the model's prediction capabilities.
- Integrate the anomaly detection logic into a loop that processes incoming data streams, ensuring real-time monitoring.