Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Predicting network performance problems enables network operators to take corrective actions in advance of service disruptions. Typically, service problems are detected by tests that compare a metric (e.g., response time) to a threshold. The authors present an online algorithm for predicting the probability of threshold violations over a time horizon. The algorithm uses two cascaded submodels. The first removes non-stationarities by employing a discrete time Kalman filter in combination with analysis of variance.  derive parameters of the Kalman filter from differential equations that describe characteristics of the data. The second submodel estimates the probability of threshold violations by using a second order autoregressive model in combination with change-point detection. Using data from a production b server,  evaluate our approach and show that it produces average accuracies that are comparable to those of an offline algorithm. Hover, our online algorithm produces predictions with considerably smaller variances. Further advantages of our approach are: (a) requiring much less data than the offline technique, one day versus multiple months; and (b) adapting to changes in the system and workloads since parameters are estimated online.
['Fan Zhang', 'Joseph L. Hellerstein']
I am writing a research paper on a idea An approach to on-line predictive detection
[{"from": "human", "value": "I am writing a research paper on a idea An approach to on-line predictive detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Predicting network performance problems enables network operators to take corrective actions in advance of service disruptions. Typically, service problems are detected by tests that compare a metric (e.g., response time) to a threshold. The authors present an online algorithm for predicting the probability of threshold violations over a time horizon. The algorithm uses two cascaded submodels. The first removes non-stationarities by employing a discrete time Kalman filter in combination with analysis of variance.  derive parameters of the Kalman filter from differential equations that describe characteristics of the data. The second submodel estimates the probability of threshold violations by using a second order autoregressive model in combination with change-point detection. Using data from a production b server,  evaluate our approach and show that it produces average accuracies that are comparable to those of an offline algorithm. Hover, our online algorithm produces predictions with considerably smaller variances. Further advantages of our approach are: (a) requiring much less data than the offline technique, one day versus multiple months; and (b) adapting to changes in the system and workloads since parameters are estimated online."}]