  Time-domain astronomy is progressing rapidly with the ongoing and upcoming
large-scale photometric sky surveys led by the Vera C. Rubin Observatory
project (LSST). Billions of variable sources call for better automatic
classification algorithms for light curves. Among them, periodic variable stars
are frequently studied. Different categories of periodic variable stars have a
high degree of class imbalance and pose a challenge to algorithms including
deep learning methods. We design two kinds of architectures of neural networks
for the classification of periodic variable stars in the Catalina Survey's Data
Release 2: a multi-input recurrent neural network (RNN) and a compound network
combing the RNN and the convolutional neural network (CNN). To deal with class
imbalance, we apply Gaussian Process to generate synthetic light curves with
artificial uncertainties for data augmentation. For better performance, we
organize the augmentation and training process in a "bagging-like" ensemble
learning scheme. The experimental results show that the better approach is the
compound network combing RNN and CNN, which reaches the best result of 86.2% on
the overall balanced accuracy and 0.75 on the macro F1 score. We develop the
ensemble augmentation method to solve the data imbalance when classifying
variable stars and prove the effectiveness of combining different
representations of light curves in a single model. The proposed methods would
help build better classification algorithms of periodic time series data for
future sky surveys (e.g., LSST).
