Merge pull request #188639 from nibaccam/automl-rocv

AutoML | ROCV new image

Author: ShannonLeavitt

articles/machine-learning/how-to-auto-train-forecast.md

@@ -48,7 +48,7 @@ You can specify separate [training data and validation data](concept-automated-m
 
 For time series forecasting, only **Rolling Origin Cross Validation (ROCV)** is  used for validation by default. Pass the training and validation data together, and set the number of cross validation folds with the `n_cross_validations` parameter in your `AutoMLConfig`. ROCV divides the series into training and validation data using an origin time point. Sliding the origin in time generates the cross-validation folds. This strategy preserves the time series data integrity and eliminates the risk of data leakage
 
-![rolling origin cross validation](./media/how-to-auto-train-forecast/ROCV.svg)
+![rolling origin cross validation](./media/how-to-auto-train-forecast/rolling-origin-cross-validation.svg)
 
 You can also bring your own validation data, learn more in [Configure data splits and cross-validation in AutoML](how-to-configure-cross-validation-data-splits.md#provide-validation-data).