There are multiple way to handle time series abnormalities-
1) If abnormalities are known, build a classification model. Use this model to detect same type of abnormalities for time series data.
2) If abnormalities are unknown, What we have done in our organisation- is a combination of clustering and classification-
First use LOF/K-means/Cook's distance to identify outliers. Convert entire data into classification problem as we have got 2 classes now- Outliers and normals. Now build a classification model and get rules( classification model ) to identify abnormality at run time ( time series data).
3) If abnormalities are unknown, During my research, most common way of identify abnormalities is to build a normal model and any deviation from normal model ( error ) is abnormal, so in ur case you forecast your time series for next hour and then compare with actual values. If error is more than expected , something abnormal is happening.
I was not able to find any direct package in python or R to do so, as nobody known what is really abnormal :P, in all the cases it has been related to outlier detection.
some useful links-