<p>Try the Bayesian blocks method, here is the paper: <strong>J. D. Scargle, J. P. Norris, B. Jackson, J. Chiang, (2012) arXiv:1207.5578.</strong></p>

<p>It is rather long paper, but you can skip to the place where they included a MatLab implementation.</p>

<p>What it does is that it splits your time series into time blocks in which the values fluctuate around some mean, that mean being different in each block.</p>

<p>Once you have the blocks, the ones which are low can be scaled up to remove the drops.</p>

<p>NOTE: there is a parameter ncp_prior , by varying it you can change the sensitivity of the method, so that it doesn't get fooled by the fluctuations, but reproduces the drop.</p>
Efficient way to elevate signal over time if it drops significantly over time
<p>I am wondering what would be the most efficient way to correct signal if it drops out significantly in some period of time. Like in the figure green signal dropped out between around 16:26 and 19:16 and I would like to elevate it to the same level like before 16:26 and after 19:16 using statistics. </p>

<p><a href="http://i.stack.imgur.com/FRI1Z.png" rel="nofollow">Please find here the figure</a></p>

<p>Thanks in advance!</p>