I understand that feature scaling is required to bring features in different magnitudes on a common scale so the model is not biased towards features with higher magnitudes. But if there is only a single feature in a feature set. For example, I have a quantity (events over time) on a time series and I'm forecasting for the future. In this case, do I need feature scaling (normalization or standardization?) or will it be unnecessary?
I searched online but couldn't find resources talking about the case where there's only a single feature in the feature set.