Quantile deep learning models for multi-step ahead time series prediction

TL;DR

This paper introduces a novel quantile regression deep learning framework for multi-step ahead time series prediction, enhancing uncertainty quantification and handling of volatility in financial data.

Contribution

It develops and evaluates a new quantile deep learning approach for multi-step time series forecasting, improving uncertainty estimation over existing models.

Findings

Quantile deep learning models outperform traditional models in high volatility scenarios.

The models effectively predict extreme values and provide uncertainty quantification.

Application to cryptocurrency data demonstrates improved handling of volatility.

Abstract

Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting. Although deep learning models have been prominent in multi-step ahead prediction, the development and evaluation of quantile deep learning models have been limited. We present a novel quantile regression deep learning framework for multi-step time series prediction. In this way, we elevate the capabilities of deep learning models by incorporating quantile regression, thus providing a more nuanced understanding of predictive values. We provide an implementation of prominent deep learning models for multi-step ahead time series prediction and evaluate their performance under high volatility and extreme conditions. We include multivariate and univariate modelling, strategies and provide a comparison with conventional deep learning models from the literature. Our models are tested on two cryptocurrencies: Bitcoin and Ethereum, using daily close-price data and selected benchmark time series datasets. The results show that integrating a quantile loss function with deep learning provides additional predictions for selected quantiles without a loss in the prediction accuracy when compared to the literature. Our quantile model has the ability to handle volatility more effectively and provides additional information for decision-making and uncertainty quantification through the use of quantiles when compared to conventional deep learning models.