Tube Loss: A Novel Approach for Prediction Interval Estimation

TL;DR

This paper introduces 'Tube Loss', a new loss function for better prediction interval estimation that guarantees coverage, allows interval adjustment, and improves performance in regression and forecasting tasks.

Contribution

The paper presents a novel Tube Loss function that improves prediction interval quality, provides theoretical guarantees, and is applicable with gradient descent in neural networks.

Findings

Tube Loss yields intervals that attain the desired confidence level asymptotically.

It allows user-controlled interval shifting to capture denser probability regions.

The method improves probabilistic forecasting accuracy on benchmark datasets.

Abstract

This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regression setup. The PIs obtained by minimizing the empirical risk based on the Tube Loss are shown to be of better quality than the PIs obtained by the existing methods in the following sense. First, it yields intervals that attain the prespecified confidence level t $\in$ (0,1) asymptotically. A theoretical proof of this fact is given. Secondly, the user is allowed to move the interval up or down by controlling the value of a parameter. This helps the user to choose a PI capturing denser regions of the probability distribution of the response variable inside the interval, and thus, sharpening its width. This is shown to be especially useful when the conditional distribution of the response variable is skewed. Further, the Tube Loss based PI estimation method can trade-off between the coverage and the average width by solving a single optimization problem. It enables further reduction of the average width of PI through re-calibration. Also, unlike a few existing PI estimation methods the gradient descent (GD) method can be used for minimization of empirical risk. Through extensive experiments, we demonstrate the effectiveness of Tube Loss-based PI estimation in both kernel machines and neural networks. Additionally, we show that Tube Loss-based deep probabilistic forecasting models achieve superior performance compared to existing probabilistic forecasting techniques across several benchmark and wind datasets. Finally, we empirically validate the advantages of the Tube loss approach within the conformal prediction framework. Codes are available at https://github.com/ltpritamanand/Tube$\_$loss.