I am new to ML and am trying to build a model to predict timeouts for a website.
The website is being monitored once a minute and the data consists of a timestamp and the response time in seconds. E.g.: 1650539220000 6.234041
.In this particular case I do not have metrics from the server itself, just everything externally available.
When collecting the metric a timeout for the http request is set to 15 seconds. Meaning that every time the server does not respond in time I have no data and I want to predict when the next time out will happen. Currently, I have about two weeks' worth of data and want to predict the next likely timeouts/anomalies within some hours.
Since missing values are a huge issue with ML models I tried to handle the timeouts by setting the value to
- 20 to have large "peaks" which does not work too well with some models
- integer-encode the data in 0 (no timeout) and 1 (timeout)
To have more features to work with I also tried splitting the timestamp into month, day, hour, minute.
So far I have tried multiple algorithms which did not perform too great
- Prophet
- LGBMRegressor (best so far)
- AutoReg
- ARIMA
I did not tune the algorithms, as I am quite new to ML.
What method, model, or approach could I try to make more accurate predictions?