Long-term prediction is still a difficult problem in data mining. People usually use various kinds of methods of Recurrent Neural Network to predict. However, with the increase of the prediction step, the accuracy of prediction decreases rapidly. In order to improve the accuracy of long-term prediction,we propose a framework Variational Auto-Encoder Conditional Generative Adversarial Network(VAECGAN). Our model is divided into three parts. The first part is the encoder net, which can encode the exogenous sequence into latent space vectors and fully save the information carried by the exogenous sequence. The second part is the generator net which is responsible for generating prediction data. In the third part, the discriminator net is used to classify and feedback, adjust data generation and improve prediction accuracy. Finally, extensive empirical studies tested with five real-world datasets (NASDAQ, SML, Energy, EEG,KDDCUP)demonstrate the effectiveness and robustness of our proposed approach.

长期预测仍然是数据挖掘中的一个难题。人们通常使用循环神经网络的各种方法进行预测。但是，随着预测步长的增加，预测的准确率迅速下降。为了提高长期预测的准确性，我们提出了一个框架变分自动编码器条件生成对抗网络（VAECGAN）。我们的模型分为三个部分。第一部分是编码器网络，可以将外源序列编码成潜在空间向量，并充分保存外源序列携带的信息。第二部分是生成器网络，负责生成预测数据。第三部分利用判别器网络进行分类反馈，调整数据生成，提高预测精度。最后，