Summary: The aim of this paper is to solve the problem that the products have few performance degradation data and nonlinear stochastic degradation paths, a grey time series combined forecasting model is proposed to build the model of degradation paths and predict the product lifetime in realtime. Firstly, in order to establish degradation paths for the same kind of products, the GM(1,1) grey model is used to forecast the trend term and the AR\((p)\) time series model is used to forecast the stochastic term. Then, similarity weights of degradation paths between the specific individuals and the same kind of products are evaluated based on the \(K\)-means clustering theory, and the specific individual degradation path model is built by weighting models of the same kind of products. Lastly, the specific individual degradation path model is updated with real-time degradation data and the lifetime is predicted. This method is used to predict lifetime of certain electronic products, and the experiment results verify its accuracy and validity.