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With the rapidly growing number of Web services, how to identify high quality Web services becomes a hot research topic. User-side QoS evaluation on Web services is a key measurement to choose the optimal Web service from a set of Web services with similar functions. The user-side QoS data is acquired through the invocation of services from different locations. However, in real world, the QoS data of Web services is sparse and not timely. Though there are already a lot of research works on the sparsity and timeliness of QoS data respectively, it's still a lack of a prediction model which can combine both of these features. In order to solve this challenging problem, we put forward a novel model, called CAPred, to provide timely QoS prediction. Our model cut the historical QoS data into several time slices. Each time slice is a 2-dimension matrix. CAPred firstly processes every time slice to fill the empty part of the matrix, then utilizes all of the historical data to predict current QoS values of Web services. And at last we demonstrate two applications, those are recommendation and selection, which utilize the QoS prediction results of our model. The experimental results indicate the high feasibility and efficiency of our model.