Tag: LSTM

Prediction & Detection

Posted on by lechuck park

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
  1. 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
  1. 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.

Change & Prediction

Posted on by lechuck park

From Claude with some prompting
This image illustrates a process called “Change & Prediction” which appears to be a system for monitoring and analyzing real-time data streams. The key components shown are:

  1. Real-time data gathering from some source (likely sensors represented by the building icon).
  2. Selecting data that has changed significantly.
  3. A “Learning History” component that tracks and learns from the incoming data over time.
  4. A “Trigger Point” that detects when data values cross certain thresholds.
  5. A “Prediction” component that likely forecasts future values based on the learned patterns.

The “Check Priorities” box lists four criteria for determining which data points deserve attention: exceeding trigger thresholds, predictions crossing thresholds, high change values, and considering historical context.

The “View Point” section suggests options for visualizing the status, grouping related data points (e.g., by location or service type), and showing detailed sensor information.

Overall, this seems to depict an automated monitoring and predictive analytics system for identifying and responding to important changes in real-time data streams from various sources or sensors.