# Detecting Pattern from Signal Data by Gaussian Mixture Model?

I'm a machine learning newbie.

I have sensor data which is generated by several sensors.

The data is a series of 'time's. (it is not labeled, in other words, I cannot know which sensor generates which 'time').

And each sensor basically generates data periodically, but sometimes it generates other data.

For example, there are two sensors (sensor A generates a 'time' every 2 seconds, sensor B generates a 'time' every 5 seconds.).

Then the data can be: {2.1, 4.3, 5.2, 6.1, 7.9, 9 (noise from one of the sensors), 10.1, 10.2} .... (there are some noises)

What I want to know is that, when another data is given, it is possible to know that the same sensors generate it? (We know the period)

What method should I have to use?

I found the Gaussian Mixture Model. Am I right to use it?

Thanks.

• So the data has (white?) additive noise, and you're trying to classify the sensor that generated the data. Can two sensors emit data at exactly the same time? If so, what is the output?
– Emre
Jun 13 '17 at 18:06
• @Emre thanks for replying. Each sensor has their own cycle and generates "encrypted and same size" output, so I cannot see the contents of them and differentiate them. Actually, what I want to know is "Is an input (a set of 'time's) generated by exactly same sensors what I'm using?".
– minkyung
Jun 15 '17 at 2:20
• So the only data is the time stamp? Do you have a separate time series for each sensor? Is there any temporal noise; do the sensors output peridiocally reliably? Do you want to distinguish between sensors with the same periodicity and different delay?
– Emre
Jun 15 '17 at 3:38
• @Emre thanks again. Yes, the only data is the timestamp. And the timestamp is mixed (not separated). There are temporal noises (the data is captured network packets). And yes, it is preferable to distinguish between sensors with the same periodicity.
– minkyung
Jun 15 '17 at 5:58