I have a dataset which contains a timestamp and a response time. I want to recognize current patterns in the dataset with labelled learning or something like that. I've been looking at multi-class classification but that only takes 1 input/row and outputs a class which it things it belongs to.
The algorithm should look at the surrounding previous data to check the correct pattern the newest input value is in.
A single input could be 45ms but you wouldn't know which pattern it belongs to unless you check all recent inputs. When it gives the class prediction a score of 0.75 or higher I can check the previous down time of the same class and give a prediction as to when the service is going down. Would be nice if 1 type of machine learning could do this all but I'm currently looking for a pattern classification algorithm.
Any help is greatly appreciated.