Outlier detection for Disk Space Usage I would like to do outlier or anomaly detection on the disk free space data. Sample dataset as below (I don't have any label dataset):

date                   free_space (GB)
2019-05-15 09:00:00    102.65
2019-05-15 09:05:00    102.69
2019-05-15 09:10:00    103.11
2019-05-15 09:15:00    102.58
2019-05-15 09:20:00    102.55 


I would like to detect whether the new value of disk space is an outlier or not. There are several outlier analysis methods (ref link): 


Box plot analysis
Based on Z score
IQR based analysis (this is similar to box plot analysis)


Above methods are more statistical approach to detect outlier. There are several ways using an unsupervised machine learning algorithm to detect outlier (ref link). For example,


K-mean
Markov Chain
Isolation Forest etc.


Which method is suitable for the above dataset? Should I implement unsupervised based machine learning algorithm or statistical approach?
Given the specific context of detecting abnormal changes in the amount of free space, I'd suggest that you use the variation over time instead of the raw amount. For instance: 

date                   free_space  variation
2019-05-15 09:00:00    102.65      NA
2019-05-15 09:05:00    102.69      0.04
2019-05-15 09:10:00    103.11      0.42
2019-05-15 09:15:00    102.58      -0.53
2019-05-15 09:20:00    102.55      -0.03


Whatever method you use, the variation is a much more relevant information to detect an unusual change than the raw size. You could also use a time window, e.g. calculate the variation over the last 30 minutes.

Personally I would simply use a heuristic for something like this: if the absolute value of the variation is higher than a threshold then label as outlier. The threshold could be a percentage of the size of the disk, e.g. 5%.