Update: The best performing algorithm so far is this one.
This question explores robust algorithms for detecting sudden peaks in realtime timeseries data.
Consider the following example data:
_{Example of this data is in Matlab format (but this question is not about the language but about the algorithm):}
p = [1 1 1.1 1 0.9 1 1 1.1 1 0.9 1 1.1 1 1 0.9 1 1 1.1 1 1 1 1 1.1 0.9 1 1.1 1 1 0.9, ...
1 1.1 1 1 1.1 1 0.8 0.9 1 1.2 0.9 1 1 1.1 1.2 1 1.5 1 3 2 5 3 2 1 1 1 0.9 1 1, ...
3 2.6 4 3 3.2 2 1 1 0.8 4 4 2 2.5 1 1 1];
You can clearly see that there are three large peaks and some small peaks. This dataset is a specific example of the class of timeseries datasets that the question is about. This class of datasets has two general features:
 There is basic noise with a general mean
 There are large ‘peaks‘ or ‘higher data points‘ that significantly deviate from the noise.
Let’s also assume the following:
 The width of the peaks cannot be determined beforehand
 The height of the peaks significantly deviates from the other values
 The algorithm updates in realtime (so updates with each new datapoint)
For such a situation, a boundary value needs to be constructed which triggers signals. However, the boundary value cannot be static and must be determined realtime based on an algorithm.
My Question: what is a good algorithm to calculate such thresholds in realtime? Are there specific algorithms for such situations? What are the most wellknown algorithms?
_{Robust algorithms or useful insights are all highly appreciated. (can answer in any language: it’s about the algorithm)}
35 Answers
Robust peak detection algorithm (using zscores)
I came up with an algorithm that works very well for these types of datasets. It is based on the principle of dispersion: if a new datapoint is a given x number of standard deviations away from some moving mean, the algorithm signals (also called zscore). The algorithm is very robust because it constructs a separate moving mean and deviation, such that signals do not corrupt the threshold. Future signals are therefore identified with approximately the same accuracy, regardless of the amount of previous signals. The algorithm takes 3 inputs: lag = the lag of the moving window
, threshold = the zscore at which the algorithm signals
and influence = the influence (between 0 and 1) of new signals on the mean and standard deviation
. For example, a lag
of 5 will use the last 5 observations to smooth the data. A threshold
of 3.5 will signal if a datapoint is 3.5 standard deviations away from the moving mean. And an influence
of 0.5 gives signals half of the influence that normal datapoints have. Likewise, an influence
of 0 ignores signals completely for recalculating the new threshold. An influence of 0 is therefore the most robust option (but assumes stationarity); putting the influence option at 1 is least robust. For nonstationary data, the influence option should therefore be put somewhere between 0 and 1.
It works as follows:
Pseudocode
# Let y be a vector of timeseries data of at least length lag+2
# Let mean() be a function that calculates the mean
# Let std() be a function that calculates the standard deviaton
# Let absolute() be the absolute value function
# Settings (the ones below are examples: choose what is best for your data)
set lag to 5; # lag 5 for the smoothing functions
set threshold to 3.5; # 3.5 standard deviations for signal
set influence to 0.5; # between 0 and 1, where 1 is normal influence, 0.5 is half
# Initialize variables
set signals to vector 0,...,0 of length of y; # Initialize signal results
set filteredY to y(1),...,y(lag) # Initialize filtered series
set avgFilter to null; # Initialize average filter
set stdFilter to null; # Initialize std. filter
set avgFilter(lag) to mean(y(1),...,y(lag)); # Initialize first value
set stdFilter(lag) to std(y(1),...,y(lag)); # Initialize first value
for i=lag+1,...,t do
if absolute(y(i)  avgFilter(i1)) > threshold*stdFilter(i1) then
if y(i) > avgFilter(i1) then
set signals(i) to +1; # Positive signal
else
set signals(i) to 1; # Negative signal
end
set filteredY(i) to influence*y(i) + (1influence)*filteredY(i1);
else
set signals(i) to 0; # No signal
set filteredY(i) to y(i);
end
set avgFilter(i) to mean(filteredY(ilag+1),...,filteredY(i));
set stdFilter(i) to std(filteredY(ilag+1),...,filteredY(i));
end
Rules of thumb for selecting good parameters for your data can be found below.
Demo
_{The Matlab code for this demo can be found here. To use the demo, simply run it and create a time series yourself by clicking on the upper chart. The algorithm starts working after drawing lag number of observations.}
Result
For the original question, this algorithm will give the following output when using the following settings: lag = 30, threshold = 5, influence = 0
:
Implementations in different programming languages:

Matlab (me)

R (me)

Golang (Xeoncross)

Python (R Kiselev)

Python [efficient version] (delica)

Swift (me)

Groovy (JoshuaCWebDeveloper)

C++ (brad)

C++ (Animesh Pandey)

Rust (swizard)

Scala (Mike Roberts)

Kotlin (leoderprofi)

Ruby (Kimmo Lehto)

Fortran [for resonance detection] (THo)

Julia (Matt Camp)

C# (Ocean Airdrop)

C (DavidC)

Java (takanuva15)

JavaScript (Dirk Lüsebrink)

TypeScript (Jerry Gamble)

Perl (Alen)

PHP (radhoo)

PHP (gtjamesa)

Dart (Sga)
Rules of thumb for configuring the algorithm
lag
: the lag parameter determines how much your data will be smoothed and how adaptive the algorithm is to changes in the longterm average of the data. The more stationary your data is, the more lags you should include (this should improve the robustness of the algorithm). If your data contains timevarying trends, you should consider how quickly you want the algorithm to adapt to these trends. I.e., if you put lag
at 10, it takes 10 ‘periods’ before the algorithm’s treshold is adjusted to any systematic changes in the longterm average. So choose the lag
parameter based on the trending behavior of your data and how adaptive you want the algorithm to be.
influence
: this parameter determines the influence of signals on the algorithm’s detection threshold. If put at 0, signals have no influence on the threshold, such that future signals are detected based on a threshold that is calculated with a mean and standard deviation that is not influenced by past signals. If put at 0.5, signals have half the influence of normal data points. Another way to think about this is that if you put the influence at 0, you implicitly assume stationarity (i.e. no matter how many signals there are, you always expect the time series to return to the same average over the long term). If this is not the case, you should put the influence parameter somewhere between 0 and 1, depending on the extent to which signals can systematically influence the timevarying trend of the data. E.g., if signals lead to a structural break of the longterm average of the time series, the influence parameter should be put high (close to 1) so the threshold can react to structural breaks quickly.
threshold
: the threshold parameter is the number of standard deviations from the moving mean above which the algorithm will classify a new datapoint as being a signal. For example, if a new datapoint is 4.0 standard deviations above the moving mean and the threshold parameter is set as 3.5, the algorithm will identify the datapoint as a signal. This parameter should be set based on how many signals you expect. For example, if your data is normally distributed, a threshold (or: zscore) of 3.5 corresponds to a signaling probability of 0.00047 (from this table), which implies that you expect a signal once every 2128 datapoints (1/0.00047). The threshold therefore directly influences how sensitive the algorithm is and thereby also determines how often the algorithm signals. Examine your own data and choose a sensible threshold that makes the algorithm signal when you want it to (some trialanderror might be needed here to get to a good threshold for your purpose).
WARNING: The code above always loops over all datapoints everytime it runs. When implementing this code, make sure to split the calculation of the signal into a separate function (without the loop). Then when a new datapoint arrives, update filteredY
, avgFilter
and stdFilter
once. Do not recalculate the signals for all data everytime there is a new datapoint (like in the example above), that would be extremely inefficient and slow in realtime applications.
Other ways to modify the algorithm (for potential improvements) are:
 Use median instead of mean
 Use a robust measure of scale, such as the median absolute deviation (MAD), instead of the standard deviation
 Use a signalling margin, so the signal doesn’t switch too often
 Change the way the influence parameter works
 Treat up and down signals differently (asymmetric treatment)
 Create a separate
influence
parameter for the mean and std (as in this Swift translation)
(Known) academic citations to this StackOverflow answer:

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Moore, J., Goffin, P., Wiese, J., & Meyer, M. (2021). An Interview Method for Engaging Personal Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(4), 128.

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Hong, Y., Xin, Y., Martin, H., Bucher, D., & Raubal, M. (2021). A ClusteringBased Framework for Individual Travel Behaviour Change Detection. In 11th International Conference on Geographic Information Science (GIScience 2021)Part II.

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Courtial, N. (2020). Fusion d’images multimodales pour l’assistance de procédures d’électrophysiologie cardiaque. Doctoral dissertation, Université Rennes.

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Other works using the algorithm from this answer

Bergamini, E. and E. MourlonDruol (2021). Talking about Europe: exploring 70 years of news archives. Working Paper 04/2021, Bruegel.

Cox, G. (2020). Peak Detection in a Measured Signal. Online article on https://www.baeldung.com/cs/signalpeakdetection.

Raimundo, D. W. (2020). SwitP: Mobile Application for RealTime Swimming Analysis.. Semester Thesis, ETH Zürich.

Bernardi, D. (2019). A feasibility study on pairing a smartwatch and a mobile device through multimodal gestures. Master thesis, Aalto University.

Lemmens, E. (2018). Outlier detection in event logs by using statistical methods, Master thesis, University of Eindhoven.

Willems, P. (2017). Mood controlled affective ambiences for the elderly, Master thesis, University of Twente.

Ciocirdel, G. D. and Varga, M. (2016). Election Prediction Based on Wikipedia Pageviews. Project paper, Vrije Universiteit Amsterdam.
Other applications of the algorithm from this answer

Avo Audit dbt package. Avo Company (nextgeneration analytics governance).

Synthesized speech with OpenBCI system, SarahK01.

Python package: Machine Learning Financial Laboratory, based on the work of De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.

Adafruit CircuitPlayground Library, Adafruit board (Adafruit Industries)

Step tracker algorithm, Android App (jeeshnair)

R package: animaltracker (Joe Champion, Thea Sukianto)
Links to other peak detection algorithms
 Realtime peak detection in noisy sinusoidal timeseries
How to reference this algorithm:
Brakel, J.P.G. van (2014). “Robust peak detection algorithm using zscores”. Stack Overflow. Available at: https://stackoverflow.com/questions/22583391/peaksignaldetectioninrealtimetimeseriesdata/22640362#22640362 (version: 20201108).
If you use this function somewhere, please credit me by using above reference. If you have any questions about the algorithm, post them in the comments below or reach out to me on LinkedIn.