Do we need to standardize when our data is univariate? In this question: What algorithms need feature scaling, beside from SVM?
 it is said that we need to standardize  so that all features are weighted equally.
But what if we only have as features: time and a count (ex: number of logins by hour). Do we still need to standardize? Why? 
 A: Time series data is frequently univariate, and some univariate time series forecasting algorithms require some sort of "standardization" (i.e using a broad definition of the term): 


*

*ARMA models require the data to be weakly stationary (that is to have constant mean and constant variance). You can use differencing and/or the Box-Cox transform to transform your univariate series into a stationary series. The 'I' in ARIMA stands for differencing, and an ARIMA model is an ARMA model with the "standardization" built into it, so to speak. 

*Using neural networks for time series forecasting, it is often recommended to standardize the data as well, either using the above mentioned transformations, a $log(x+1)$ transform, or a sometimes just a simple ${x}^T = \frac{x - E(x)}{VAR(x)}$.   

A: Edit:
I believe your variables are still subject to standardization depending on the model due to being on different scales. Time series data needs transformed before use in ML, so going with your example (hours) will be something like an INT between 0-24, while logins will be on the scale of something like 0-10,000.
