Summary of Tube Loss: a Novel Approach For Prediction Interval Estimation and Probabilistic Forecasting, by Pritam Anand et al.
Tube Loss: A Novel Approach for Prediction Interval Estimation and probabilistic forecasting
by Pritam Anand, Tathagata Bandyopadhyay, Suresh Chandra
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel loss function called Tube Loss is proposed for simultaneously estimating bounds of a Prediction Interval (PI) in regression setup and generating probabilistic forecasts from time series data, solving a single optimization problem. The paper demonstrates that PIs obtained by minimizing the empirical risk based on Tube Loss are of better quality than existing methods, with intervals achieving prespecified confidence levels asymptotically. The method also allows for user-controlled interval width adjustment, which is useful when conditional response variable distributions are skewed. Additionally, the approach enables trading off coverage and average PI width through re-calibration and uses gradient descent (GD) for minimization of empirical risk. Experimental results show the efficacy of Tube Loss-based PI estimation in kernel machines, neural networks, deep networks, and probabilistic forecasting tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to find Prediction Intervals (PIs) that are more accurate and flexible than existing methods. The “Tube Loss” approach combines two important tasks: estimating the bounds of PIs and generating probabilistic forecasts from time series data. It does this by solving just one optimization problem, making it more efficient. This new method is shown to work well in different types of models, such as kernel machines, neural networks, and deep networks. |
Keywords
» Artificial intelligence » Gradient descent » Loss function » Optimization » Regression » Time series
