Earticle

초록

영어

In this paper, we present a new approach to time series forecasting, especially predicting stock data. Despite there have been lots of attempts for predicting future stock prices by using machine learning, performances have not been fine since many noises exist in stock data. In this work, we develop a novel method that using auxiliary classifier generative adversarial network to predict future stock prices. Basically, generative adversarial network suffers noise as an input of generator. This means generative adversarial network can be trained efficiently with the noisy data. In practice, our new method shows remarkable results compared to conventional other methods.

목차

Abstract
I. INTRODUCTION
II. METHOD
A. Generative adversarial networks
B. Auxiliary classifier GAN
C. Wasserstein distance
D. Model architecture
III. RESULTS & DISCUSSION
A. Data
B. Experiment
IV. CONCLUSIONS
ACKNOWLEDGMENT
REFERENCES

저자

• Jiwook Kim [ School of Electrical and Electronics Engineering, Chung-Ang University ]
• Minhyeok Lee [ School of Electrical and Electronics Engineering, Chung-Ang University ]

참고문헌