which algorithm is suits for my time series data? : I am working in monitoring team, we do monitor of our client load on our tools.
We recorded latency with respective to timeseries. 
Initially i kept static threshold to raise the anomaly detection. However, it doesn't work if seasonality occurs. Now, I am planning to apply ML on my data. 
My data looks like [enter image description here][1]

volume_nfs_ops	timestamp	mount_point
2103	6/28/2018 3:16	/slowfs/us01dwt2p311
12440	6/28/2018 6:03	/slowfs/us01dwt2p311
14501	6/28/2018 14:20	/slowfs/us01dwt2p311
12482	6/28/2018 14:45	/slowfs/us01dwt2p311
10420	6/28/2018 18:09	/slowfs/us01dwt2p311
7203	6/28/2018 18:34	/slowfs/us01dwt2p311
14104	6/28/2018 21:58	/slowfs/us01dwt2p311
6996	6/29/2018 7:35	/slowfs/us01dwt2p311
11282	6/29/2018 8:39	/slowfs/us01dwt2p311

When I do google, I came up ARIMA is best model for time series. I am towards mathematics and could figure whether respective ARIMA is good for my data set.
My question which algorithm is best to implement in python. 
which factors should I consider to find anomaly ?
  [1]: https://i.stack.imgur.com/QGMYk.png