Efficient classification of periodic time-series data has very important significance for various application fields like healthcare, meteorology and industrial production. However, the training data for such tasks may become insufficient due to complex circumstances like sensor failures. An output-based transfer learning for RVFL model (OTL-RVFL) was developed to improve random vector functional-link networks (RVFL) so as to achieve enhanced classification performance from insufficient periodic time-series data. The basic idea was to leverage RVFL as the base classification model, characterized by its simple architecture, efficient training and strong classification performance. Firstly, the basic RVFL classification model was built along each period for periodic time-series data. Secondly, for each period with insufficient data, an OTL-RVFL model was built to transfer the outputs of the corresponding basic RVFL models trained on one or more previous periods and learn the model parameters analytically in closed-form solution, thereby enhancing the classification performance on the current period’s time-series data. Experimental results on public datasets demonstrate that the proposed method effectively combines RVFL with transfer learning, significantly improving the classification performance for insufficient periodic time-series data classification while retaining RVFL’s remarkable advantages in training efficiency.