# Unsupervised learning algorithms to detect anomaly in waves

I have a sample of graphs (more than 10000...). that look like in the image below: I am searching for an unsupervised learning algorithms that can help me to detect anomalous observations.

Here what I suggest for beginning: for every observation I have a collection of points $(x,y)$. With this collection, I find Fourier series with regression (I compute coefficients with the base $\{1,\sin(x),\cos(x),\sin(2x),\cos(2x)\dots\}$). Now I have a set of coefficients instead of waves.

Somebody have an idea how to detect anomaly?

• Do you have the data the graphs were made from, or just the graphs? – JRE Dec 1 '14 at 14:39
• I'm not sure Fourier is the best approach. How about looking at continuity? If two values of x are close but the ys are vastly different, that could be an outlier? – barrycarter Dec 1 '14 at 17:36
• @JRE I have the data. – dmitriy Dec 6 '14 at 10:20
• @barrycarter But what is the statistics? How I detect anomaly with your method? – dmitriy Dec 6 '14 at 10:24
• Are you looking for anomaly in a single graph or for anomal graph compared to other graphs? – Vertex Apr 2 '15 at 12:31