Acrolinx

Author: ttorble

articles/machine-learning/v1/how-to-auto-train-forecast.md

@@ -295,7 +295,7 @@ Next, let's examine the image, which plots the original series in first differen
 
 AutoML Machine learning models cannot inherently deal with stochastic trends, or other well-known problems associated with non-stationary time series. As a result, their out of sample forecast accuracy is "poor" if such trends are present.
 
-AutoML automatically analyzes time series dataset to check whether it's stationary or not. When non-stationary time series are detected, AutoML applies a differencing transform automatically to mitigate the affect of non-stationary time series.
+AutoML automatically analyzes time series dataset to check whether it's stationary or not. When non-stationary time series are detected, AutoML applies a differencing transform automatically to mitigate the effect of non-stationary time series.
 
 ## Run the experiment 
 
@@ -412,7 +412,7 @@ mm_paramters = ManyModelsTrainParameters(automl_settings=automl_settings, partit
 
 ### Hierarchical time series forecasting
 
-In most applications, customers have a need to understand their forecasts at a macro and micro level of the business. Forcasts can be predicting sales of products at different geographic locations, or understanding the expected workforce demand for different organizations at a company. The ability to train a machine learning model to intelligently forecast on hierarchy data is essential. 
+In most applications, customers have a need to understand their forecasts at a macro and micro level of the business. Forecasts can be predicting sales of products at different geographic locations, or understanding the expected workforce demand for different organizations at a company. The ability to train a machine learning model to intelligently forecast on hierarchy data is essential. 
 
 A hierarchical time series is a structure in which each of the unique series is arranged into a hierarchy based on dimensions such as, geography or product type. The following example shows data with unique attributes that form a hierarchy. Our hierarchy is defined by: the product type such as headphones or tablets, the product category, which splits product types into accessories and devices, and the region the products are sold in.