+
+### The issue is that I wanted forecasts for a very niche set of query i.e state=WA,store_id=1,product_category=B,SKU=B2,but I had to wait for the whole traning to get over hence I have come up with this class-based solution to address this issue by natively using Azure Automl
+
+### What really happens is that I dynamically slice the dataset basis the heirarchy variables the user passes upon which it requires the forecast for,then using the sliced dataset I train the model and give out predictions for the same.There is an additional caching feature to avoid retraining of models on the same heirarchy variables.
+
+### This is benificial in 2 ways:
+
+- ### We can work with the standard compute resource and don't need to acquire more expensive compute resources
+- ### Traning time reduced significantly, as we train only on what's needed at this point in time.
+
+### This might not be exactly accurate(due to run time slicing of dataset),but it's still a good starting point to do hts forecasting with low compute resources.