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

• URL: http://arxiv.org/abs/2411.15674v1
• Date: Sun, 24 Nov 2024 00:00:10 GMT
• Title: Quantile deep learning models for multi-step ahead time series prediction
• Authors: Jimmy Cheung, Smruthi Rangarajan, Amelia Maddocks, Xizhe Chen, Rohitash Chandra,
• Abstract summary: We present a novel quantile regression deep learning framework for multi-step time series prediction. We provide an implementation of deep learning models for multi-step ahead time series prediction. We evaluate their performance under high volatility and extreme conditions.
• Score: 0.15833270109954137
• License:
• 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.

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