# Find control chart limits based on existing data

I'd like to produce a control chart that tells me if a given process will be within bounds in the future or not.

Currently, the process gives me simple timeseries data, per minute

Time  Latency in sec
15:25 1000
15:26 1200
15:27 950
...


I have about 1000 of these per day. We can take the assumption that the process works within bounds for now.

Based on this, I calculated the mean and standard deviation of the data points. Then I calculated ±1 standard deviation, ±2 standard deviations, ±3 standard deviations and charted the data.

If the data were normally distributed, one can assume that about 68% = 680 data points are within 1 stdev, 95% within 2 stdev, etc. but this was NOT the case. So the data is NOT normally distributed, which I already expected.

Now I read somewhere that the data doesn't need to be normally distributed. But we need to find a model such that the residuals need to be normally distributed.

Is that the way to go here? Find a model that gives me a normal distribution based on the data I have?

What I did in the meantime in my control chart is to create thresholds which I created in such a way that the 1000 datapoints indeed follow the 68-95-99 rule. But for example the first threshold is from ±4500 and the second threshold is from ±7000. Then 68% of the data is within the first control line and 95% of the data is within the second control line. Is this approach not valid?