Prediction & Detection

From Claude with some prompting
This image illustrates a Prediction and Detection system for time series data. Let me break down the key components:

1. Left Large Box (Learning and Prediction Section):

• Blue line: Actual Temperature data
• Red dotted line: Predicted Temperature data
• Uses time series prediction models like LSTM, ARIMA, and Prophet for learning

2. Top Right (Threshold-based Anomaly Detection):

• “Abnormal Detection with Threshold”
• Detects abnormal temperature changes based on threshold values
• The area marked with a red circle shows where values exceed the threshold
• Includes “Warning” and “Critical” threshold levels

3. Bottom Right (Pattern-based Anomaly Detection):

• “Anomaly Detection with Predict-Pattern”
• Compares predicted patterns with actual data to detect anomalies
• The area marked with a green circle shows where actual data deviates from the predicted pattern

The system detects anomalies in two ways:

1. When values exceed predetermined thresholds
2. When actual data significantly deviates from predicted patterns

This type of system is particularly useful in:

• Industrial monitoring
• Equipment maintenance
• Early warning systems
• Quality control
• System health monitoring

The combination of prediction and dual detection methods (threshold and pattern-based) provides a robust approach to identifying potential issues before they become critical problems.