Argthtjtr

I have experimental data with periodic sharp artefact spikes, which mask the real signal that could be seen as several negative peaks in the first third part on the curve:

Is it possible somehow to filter these spikes? or at least significantly reduce them? Here is data set:

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Is it possible somehow to filter these spikes?

In that question "filter" can be interpreted in several ways. Instead of signal filtering (used in the previous answers) you can identify and remove the outliers.

Import["https://raw.githubusercontent.com/antononcube/MathematicaForPrediction/master/OutlierIdentifiers.m"]



(* This finds the outliers positions. *)

otlPos = OutlierPosition[data[[All, 2]], SPLUSQuartileIdentifierParameters];



(* Plot the data in blue and the outliers in red. *)

Show[ListLinePlot[data, PlotRange -> All], 

 ListPlot[data[[otlPos]], PlotStyle -> {Red}]]

(* Plot the data with the outliers removed. *)

ListLinePlot[data[[Complement[Range[Length[data]], otlPos]]], 

 PlotRange -> All]

You might try a "moving median". Unfortunately the decision for an optimal window width is "I'll know it when I see it" kinda thing. In other words, it takes subject matter expertise and restraint.

Manipulate[

 ListPlot[Table[{data[[n, 1]], Median[data[[n - ww ;; n + ww, 2]]]},

   {n, ww + 1, Length[data] - ww}], Joined -> True, PlotRange -> All],

 {{ww, 2, "Window width"}, 2, 200, 2, Appearance -> "Labeled"}]

How about a running median filter?

With[{

   time = Median /@ Partition[data[[All, 1]], 5, 1],

   ampl = Median /@ Partition[data[[All, 2]], 5, 1]}, 

 ListPlot[Transpose[{time, ampl}], Joined -> True, PlotRange -> All]]