시계열의 상태 변환 라벨링과 이미지 변환 기반 딥러닝을 이용한 단기 고변동성 가상화폐 가격의 예측력 개선 연구

초록

We propose a framework to improve the prediction power of highly volatile and relatively short time series data, which works by transforming the target value to target regimes evaluated from time series information. Regime transformation labeling of time series in an window values ​ employes a homoge neous regime switching model. Since regime labels exist for windows, we use computer vision deep learning models through image transformation with the Gramian Angular Field and data augmentation to improve prediction power. VGGNet, GoogleNet, and ResNet-50 are used for the deep learning models. For empirical analysis, we use price data from the cryptocurrency market, which is known for its high volatility and limited data availability. In particular, we assess performance using Trump-coin, Melania coin and ChillGuy representative memetic coins. The experimental results show high prediction per formance, as measured by ROC-AUC. Especially, ResNet-50 achieves ROC-AUC of 0.96- 0.997. These results suggest that the framework proposed in this study can be effectively utilized in non-traditional asset classes with low initial trading volume or rapid market changes.

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