In order to accurately predict key variables of complex industrial processes, it is necessary to establish reliable data-driven soft sensing models. The accuracy of soft sensors can be compromised when we meet limited process data or a high sample repetition rate with insufficient valuable information. Generally, virtual sample generation (VSG) methods such as variational autoencoder (VAE) prove to be effective strategies for addressing the small data problem. However, it is unavoidable to generate similar samples by traditional VSG methods. Moreover, the time-relevant features are usually ignored since individual samples are generated according to the original data distribution. To improve the data augmentation performance of complex processes with limited time-series data, this article introduces a novel VSG method based on time conditional VAE (TimeCVAE). By incorporating labeled data into the VSG process, the generator ensures that the generated virtual samples are closer to the distribution of real samples. Besides, time-related modules are designed as an important part of TimeCVAE model. These developments enhance the quality of the generated data and assist to improve the prediction accuracy of the soft sensor with small data. The effectiveness of the proposed method is validated through two industrial applications.