Research on the application of conditional generative adversarial nets in economic time series data analysis

The processing of time series data is the key technical field of financial data analysis. With the continuous development of computing science, deep learning has a revolutionary impact on the traditional computing model. Among them, the generative adversarial nets GANs has achieved desirable results in the field of data generation. Revolving around the conditional generative adversarial nets cGANs, an effective Bi-LSTM generator, CNN discriminator and data processing method are designed in this paper. Also, the experiments on two economic datasets including the stock and commodity price are implemented. The results show that compared with the traditional model, the prediction performance of this research method witnesses a great improvement and it can be employed to better deal with the analysis task of non-stationary data, which is a significant point contributed by the research. In addition, the details associated with the generator mode and GANs model optimization are reported and discussed in combination with the actual situation of the experiment, and the existing problems are further explained and discussed.