Cryptocurrency Price Forecasting using Variational Autoencoder with Versatile Quantile Modeling

In recent years, there has been a growing interest in probabilistic forecasting methods that offer more comprehensive insights by considering prediction uncertainties rather than point estimates. This paper introduces a novel variational autoencoder learning framework for multivariate distributional forecasting. Our approach employs distributional learning to directly estimate the cumulative distribution function of future time series conditional distributions using the continuous ranked probability score. By incorporating a temporal structure within the latent space and utilizing versatile quantile models, such as the generalized lambda distribution, we enable distributional forecasting by generating synthetic time series data for future time points. To assess the effectiveness of our method, we conduct experiments using a multivariate dataset of real cryptocurrency prices, demonstrating its superiority in forecasting high-volatility scenarios.