Abstract
The quality of information communication is the key guarantee for the normal operation of a system. Information filtering techniques and methods are important for system failure detection. Kalman filtering is a widely used method of optimal estimation as it is easy to computer programming and data can be updated and processed in real time. In this paper, the financial information system is taken for application of Kalman filtering in detecting the failure. Dynamic detecting models are established as the integration of a process model and a discriminant model. The process model is used to describe a dynamic process of financial failure and the discriminant model is used to describe discriminant rules. A real-time detection method is developed based on Kalman filtering and a general n-step-ahead forecasting algorithm is improved in order for the prospective forecast. The dynamic forecasting and detecting process is implemented through computer programming using the data of China’s communications industries. The results have proved the accuracy and advance of detecting financial failure in such case.
Published Version
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