How to decide for the contamination value (proportion of the outliers) in my dataset? I should decide on the contamination value while using the Isolation Forests algorithm (I am using the sklearn implementation). Otherwise, sklearn's default is 0.1. 

I am worried if I decide for this parameter with experimenting on my current dataset, it will differ by time as I would be receiving new data points very soon. 

How can I know that this parameter better than any other, just by experimenting it with outlier removal? 

Thanks.
If you want to experiment something like that I think you can add some outliers into your data for that respective field and see what are its effects.

If you find that these are prone to outliers(most of of the features are), then you can handle situation by removing such outliers before appending to the existing data.

What is your target variable?
Did you do any kind of Correlation analysis/ Predictor Importance?