ForecastingEvaluationMetrics

Metrics for forecasting evaluation results.

Fields
quantileMetrics[] object (QuantileMetricsEntry)

The quantile metrics entries for each quantile.

rootMeanSquaredError number

Root Mean Squared Error (RMSE).

meanAbsoluteError number

Mean Absolute Error (MAE).

meanAbsolutePercentageError number

Mean absolute percentage error. Infinity when there are zeros in the ground truth.

rSquared number

Coefficient of determination as Pearson correlation coefficient. Undefined when ground truth or predictions are constant or near constant.

rootMeanSquaredLogError number

Root mean squared log error. Undefined when there are negative ground truth values or predictions.

weightedAbsolutePercentageError number

Weighted Absolute Percentage Error. Does not use weights, this is just what the metric is called. Undefined if actual values sum to zero. Will be very large if actual values sum to a very small number.

rootMeanSquaredPercentageError number

Root Mean Square Percentage Error. Square root of MSPE. Undefined/imaginary when MSPE is negative.

JSON representation 
{
  "quantileMetrics": [
    {
      object (QuantileMetricsEntry)
    }
  ],
  "rootMeanSquaredError": number,
  "meanAbsoluteError": number,
  "meanAbsolutePercentageError": number,
  "rSquared": number,
  "rootMeanSquaredLogError": number,
  "weightedAbsolutePercentageError": number,
  "rootMeanSquaredPercentageError": number
}

QuantileMetricsEntry

Entry for the Quantiles loss type optimization objective.

Fields
quantile number

The quantile for this entry.

scaledPinballLoss number

The scaled pinball loss of this quantile.

observedQuantile number

This is a custom metric that calculates the percentage of true values that were less than the predicted value for that quantile. Only populated when [optimizationObjective][google.cloud.aiplatform.publicfiles.trainingjob.definition.AutoMlForecastingInputs.optimization_objective] is minimize-quantile-loss and each entry corresponds to an entry in [quantiles][google.cloud.aiplatform.publicfiles.trainingjob.definition.AutoMlForecastingInputs.quantiles] The percent value can be used to compare with the quantile value, which is the target value.

JSON representation 
{
  "quantile": number,
  "scaledPinballLoss": number,
  "observedQuantile": number
}