Question: I have a time series of values that may be described as normally skewed or distributed. This is collected from varying degrees of positive and negative integers over time. I then inspect the histogram to see the distribution of these integers and sometimes find an extremely long tail in either the positive or negative direction. 

My question is, since I want to identify these tails, are there methods to determine the point at which the distribution becomes 'long-tailed'? As in is there is a way to quantify the thickness of the tail of a distribution?

Answer: 
You might want to check out extreme value theory (EVT) at this link:

<https://www.statisticshowto.com/extreme-value-distribution/>
`scipy` offers functions to calculate the [skewness](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.skew.html) and [kurtosis](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kurtosis.html) of distributions:
`scipy.stats.kurtosis()` and `scipy.stats.skew()`. Your interest probably mainly lies in the skewness.