Informatization grows rapidly in all walks of life, going with the enhancement of dependence on IT systems. It is of vital importance to ensure the safe and stable running of the system especially in the field of finance. This paper puts forward a machine learningbased framework for predicting the occurrence of the alarm cases of a financial IT system. We extracted the features from the system logs then build three sub modules which are time-series prediction module, alarm classification module and level division module that composing the whole work flow. We take multiple methods to deal with the problems facing the obstacles in each module. We built the time-series prediction model in terms of time and accuracy performance. To gain higher performance, we introduced ensemble learning methods in designing alarm classifier and alleviated the class-imbalance problem in alarm level division process. The evaluation results from all sides show that our framework could be exploited for real time applications with the veracity and reliability ensured.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.