I am not sure how to ask or search for this, any key words or concepts are appreciated. If the difference of nature of these two data forms can be elaborated would be great.
Usually, you would consider either normalizing or standardizing your data. Normalization is to rescale your data between 0-1, while standardizing your data is to reshape it into normal distribution. While the latter one is more robust for new data, it requires the prior knowledge that your data follows normal distribution which is not true in many cases. Normalization requires you know the min and max of your data, but generally more applicable to most cases.
For time series data, the above two methods still apply, but you would want to first make sure you are dealing with stationary data. Otherwise the "scale" your model learned from the training set may not apply well for inference.
There are another two methods for time series data: power transformation and difference transformation.
Power transform is used to remove a changing variance. Log transform, Box-Cox transform and Yeo-Johnson transform are some variants.
Difference transform is used to remove trend or seasonality.