Prediction and Prediction Intervals in Exchange Rates: A Generative Approach Using Variational Autoencoders

• 발행기관: 한국계량경제학회
• 저자명: Soohyon Kim
• 간행물 정보: 『JOURNAL OF ECONOMIC THEORY AND ECONOMETRICS』Vol.35 No.4, 33~54쪽, 전체 22쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

In this study, we explore the use of generative models for time series prediction and construction of prediction intervals, addressing the challenge of quantifying uncertainty in deep learning models. Specifically, we employ a Variational Autoencoder (VAE), a form of a Bayesian neural network also a part of generative AI models, to model and generate latent factors for the exchange rates of ten currencies. These latent factors enable the approximate reconstruction of the exchange rate series through the decoder part of VAE. By generating a thousand sets of latent factors and reconstructing exchange rates, we create prediction intervals through a Multi-Layer Perceptron (MLP) applied to the reconstructed series. This approach provides valuable insights into evaluation of the uncertainty associated with time series predictions using neural networks.

목차

1. INTRODUCTION

2. LITERATURE REVIEW

3. METHODOLOGY

4. CONCLUSION

REFERENCES

키워드

Time series prediction prediction interval generative model uncertainty measure