Typo fixes

Author: cartacioS

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

@@ -223,7 +223,7 @@ View the [Beverage Production Forecasting notebook](https://github.com/Azure/Mac
 ### Target Rolling Window Aggregation
 Often the best information a forecaster can have is the recent value of the target. Creating cumulative statistics of the target may increase the accuracy of your predictions. Target rolling window aggregations allows you to add a rolling aggregation of data values as features. To enable target rolling windows set the `target_rolling_window_size` to your desired integer window size. 
 
-An example of this can be seen when predicting energy demand. You might add a rolling window feature of three days to account for themral changes of heated spaces. In the example below, we've create this window of size three by setting `target_rolling_window_size=3` in the `AutoMLConfig` constructor. The table shows feature engineering that occurs when window aggregation is applied. Columns for minimum, maximum, and sum are generated on a sliding window of three based on the defined settings. Each row has a new calculated feature, in the case of the time-stamp for September 8, 2017 4:00am the maximum, minimm, and sum values are calculated using the demand values for Septeber 8, 2017 1:00AM - 3:00AM. This window of three shifts along to populate data for the remaining rows.
+An example of this can be seen when predicting energy demand. You might add a rolling window feature of three days to account for thermal changes of heated spaces. In the example below, we've created this window of size three by setting `target_rolling_window_size=3` in the `AutoMLConfig` constructor. The table shows feature engineering that occurs when window aggregation is applied. Columns for minimum, maximum, and sum are generated on a sliding window of three based on the defined settings. Each row has a new calculated feature, in the case of the time-stamp for September 8, 2017 4:00am the maximum, minimum, and sum values are calculated using the demand values for September 8, 2017 1:00AM - 3:00AM. This window of three shifts along to populate data for the remaining rows.
 
 ![alt text](./media/how-to-auto-train-forecast/target-roll.svg)