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Deep Learning Quantile Regression for Interval-Valued Data Prediction
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AI
DOI：10.1002/for.3271.png)
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Abstract
En 中文
Interval-valued data are a special symbolic data, which contains rich information. The prediction of interval-valued data is a challenging task. In terms of predicting interval-valued data, machine learning algorithms typically consider mean regression, which is sensitive to outliers and may lead to unreliable results. As an important complement to mean regression, in this paper, a quantile regression artificial neural network based on a center and radius method (QRANN-CR) is proposed to address this problem. Numerical studies have been conducted to evaluate the proposed method, comparing with several traditional models, including the interval-valued quantile regression, the center method, the MinMax method, and the bivariate center and radius method. The simulation results demonstrate that the proposed QRANN-CR model is an effective tool for predicting interval-valued data with higher accuracy and is more robust than the other methods. A real data analysis is provided to illustrate the application of QRANN-CR.
Keywords:
deep neural network
interval-valued data
quantile regression
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