AI Integration Demo

The SciChart Engine provides a powerful bridge for integrating your own AI models. This allows for real-time predictions, forecasting, and advanced signal analysis directly in the browser.

Interactive Forecasting

Below is a demonstration of an AI-powered Forecasting Tool. It uses the PluginMLIntegration to run a mock LSTM inference on historical data and visualizes both the projected trend and the uncertainty (confidence interval).

AI Forecasting

Neural Network prediction based on historical data.

How it Works

1. Data Extraction: The plugin extracts raw X/Y data from any registered chart series.
2. External Inference: The data is passed to your registered model (which could use @tensorflow/tfjs, onnxruntime-web, or a remote API).
3. Synchronous Visualization: The resulting prediction is returned to the plugin, which renders it using the high-performance overlay system.

Example: Predictive Maintenance

Imagine monitoring laboratory equipment. You can run an anomaly detection model every few seconds:

const result = await chart.ml.runInference('anomaly-model', 'sensor-series');

if (result.metadata.anomalyScore > 0.8) {
  chart.ml.visualizeResults(result, {
    lineStyle: { color: '#ef4444' } // High alert red
  });
}

Features

• Confidence Intervals: Built-in support for visualizing prediction uncertainty.
• Low Overload: Predictions are rendered on an overlay layer, keeping the main WebGL engine focused on high-speed data updates.
• Model Agnostic: Works with any JavaScript-based ML library.