convert to png, add lightbox

Author: GitHubber17

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

@@ -469,7 +469,7 @@ Recent values of the target are often impactful features in a forecasting model.
 
 Consider an energy demand forecasting scenario where weather data and historical demand are available. The table shows resulting feature engineering that occurs when window aggregation is applied over the most recent three hours. Columns for *minimum*, *maximum,* and *sum* are generated on a sliding window of three hours based on the defined settings. For instance, for the observation valid on September 8, 2017 4:00am, the maximum, minimum, and sum values are calculated by using the *demand values* for September 8, 2017 1:00AM - 3:00AM. This window of three hours shifts along to populate data for the remaining rows. For more information and examples, see the [Lag features for time-series forecasting in AutoML](concept-automl-forecasting-lags.md).
 
-:::image type="content" source="./media/how-to-auto-train-forecast/target-roll.svg" alt-text="Diagram of a table with data that shows the target rolling window and the values in the Demand column highlighted.":::
+:::image type="content" source="./media/how-to-auto-train-forecast/target-roll.png" alt-text="Diagram of a table with data that shows the target rolling window and the values in the Demand column highlighted." lightbox="./media/how-to-auto-train-forecast/target-roll.png":::
 
 You can enable lag and rolling window aggregation features for the target by setting the rolling window size and the lag orders you want to create. The window size was three in the previous example. You can also enable lags for features with the `feature_lags` setting. In the following example, all of these settings are set to `auto` to instruct AutoML to automatically determine settings by analyzing the correlation structure of your data: