I have a sample dataset with the following structure:
- Date
- Store ID
- Product ID
- Quantity Sold
I have the data for 10 months and I am trying to predict the quantity required for the 11th month. But in my case I want the prediction for different product IDs (about 100) in different stores. So can I use ARIMA and SARIMA models to do this forecasting or should I use Machine Learning Models. Which is better and Why? If I am using ARIMA and SARIMA , can it consider the product ID and Store ID into consideration for the forecasting?
I have tried to predict using ARIMA but according to the programs I referred in kaggle they are just considering the date and sales column to predict for future.