PluginMLIntegration

The PluginMLIntegration provides a standardized interface for connecting machine learning models (e.g., Tensorflow.js, ONNX) to the SciChart Engine. It handles data extraction, asynchronous inference, and high-performance visualization of predictions and confidence intervals.

Core API

registerModel(model)

Register a custom model implementation that satisfies the MLModelAPI interface.

chart.ml.registerModel({
  id: 'my-nn-forecaster',
  name: 'Forecasting Model',
  type: 'forecasting',
  async predict(data) {
    // data.x and data.y are plain arrays extracted from series
    const prediction = await myLoadedModel.predict(tf.tensor(data.y));
    return {
      x: futureXArray,
      y: predictionArray,
      confidence: confidenceIntervalArray
    };
  }
});

runInference(modelId, seriesId)

Runs analysis on a specific data series. It returns the PredictionResult directly.

visualizeResults(result, config)

Renders the result on the chart overlay. This is extremely efficient as it avoids creating new heavy-weight series for transient predictions.

• showConfidenceInterval: Renders a translucent band around the prediction.
• intervalOpacity: Control the transparency of the confidence band.
• lineStyle: Customize the appearance of the prediction curve.

Scientific Application

Specifically designed for:

• Real-time Signal Denoising: Using autoencoders to predict clean signals.
• Anomaly Detection: Visualizing probability scores across a time series.
• Electrochemical Forecasting: Predicting peak positions in future CV cycles.
• Trend Extrapolation: Using LSTMs to forecast multi-variable trends.