I have data values for events per second (EPS) present in log files pertaining to various devices. The idea is that these values should help us observe a trend and create thresholds for specific times during the day, and specific weekdays. If the values observed in the past hour exceeds the threshold, an alert is generated.
What we have tried: We have tried using a trimmed mean method to average out the EPS values for specific subsets of time and day. However, the data is more chaotic than we thought and the thresholds are falling short such that we have a lot of false positives. So we are now looking at machine learning algorithms to see if they offer better performance.
The idea is to fit a model to the past data and attempt to predict future values. Alerts are generated on noticing deviations from the predicted values coming out from the machine learning algorithm. We have looked at 'SVM', 'MLP', 'Neural Networks' etc., however we do not know which approach would work best since we do not have significant data science knowledge. Any recommendations are appreciated.
Our data looks like this:
Is there a tool (like Weka or libsvm) that would allow us to input this data, train the model, and make future predictions?