Time Series:Outlier Detection
You could compute mean and standard deviations in sliding windows, and use those to remove outliers. 

For example, taking windows of, say, length 100, you can compute the mean and std for for these 100 successive observations, and see whether any point falls above the 3 sigma rule. In this case, the circled outlier would still be recognized as such, while the others should not, as they are not so outlying w.r.t. neighboring data (i.e. within the window that includes them and their neighboring observations).