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Smoothing

In W&B line plots, we support three types of smoothing:

See these live in an interactive W&B report.

Exponential Moving Average (Default)​

Exponential smoothing is a technique for smoothing time series data by exponentially decaying the weight of previous points. The range is 0 to 1. See Exponential Smoothing for background. There is a de-bias term added so that early values in the time series are not biased towards zero.

The EMA algorithm takes the density of points on the line (i.e. the number of y values per unit of range on x-axis) into account. This allows consistent smoothing when displaying multiple lines with different characteristics simultaneously.

Here is sample code for how this works under the hood:

const smoothingWeight = Math.min(Math.sqrt(smoothingParam || 0), 0.999);
let lastY = yValues.length > 0 ? 0 : NaN;
let debiasWeight = 0;

return yValues.map((yPoint, index) => {
  const prevX = index > 0 ? index - 1 : 0;
  // VIEWPORT_SCALE scales the result to the chart's x-axis range
  const changeInX =
    ((xValues[index] - xValues[prevX]) / rangeOfX) * VIEWPORT_SCALE;
  const smoothingWeightAdj = Math.pow(smoothingWeight, changeInX);

  lastY = lastY * smoothingWeightAdj + yPoint;
  debiasWeight = debiasWeight * smoothingWeightAdj + 1;
  return lastY / debiasWeight;
});

Here's what this looks like in the app:

Gaussian Smoothing​

Gaussian smoothing (or gaussian kernel smoothing) computes a weighted average of the points, where the weights correspond to a gaussian distribution with the standard deviation specified as the smoothing parameter. See . The smoothed value is calculated for every input x value.

Gaussian smoothing is a good standard choice for smoothing if you are not concerned with matching TensorBoard's behavior. Unlike an exponential moving average the point will be smoothed based on points occurring both before and after the value.

Here's what this looks like in the app:

Running Average​

Running average is a smoothing algorithm that replaces a point with the average of points in a window before and after the given x value. See "Boxcar Filter" at https://en.wikipedia.org/wiki/Moving_average. The selected parameter for running average tells Weights and Biases the number of points to consider in the moving average.

Consider using Gaussian Smoothing if your points are spaced unevenly on the x-axis.

The following image demonstrates how a running app looks like in the app:

Exponential Moving Average (Deprecated)​

The TensorBoard EMA algorithm has been deprecated as it cannot accurately smooth multiple lines on the same chart that do not have a consistent point density (number of points plotted per unit of x-axis).

Exponential moving average is implemented to match TensorBoard's smoothing algorithm. The range is 0 to 1. See Exponential Smoothing for background. There is a debias term added so that early values in the time series are not biases towards zero.

Here is sample code for how this works under the hood:

  data.forEach(d => {
    const nextVal = d;
    last = last * smoothingWeight + (1 - smoothingWeight) * nextVal;
    numAccum++;
    debiasWeight = 1.0 - Math.pow(smoothingWeight, numAccum);
    smoothedData.push(last / debiasWeight);

Here's what this looks like in the app:

Implementation Details​

All of the smoothing algorithms run on the sampled data, meaning that if you log more than 1500 points, the smoothing algorithm will run after the points are downloaded from the server. The intention of the smoothing algorithms is to help find patterns in data quickly. If you need exact smoothed values on metrics with a large number of logged points, it may be better to download your metrics through the API and run your own smoothing methods.

Hide original data​

By default we show the original, unsmoothed data as a faint line in the background. Click the Show Original toggle to turn this off.

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