Taming the Long Tail of Deep Probabilistic Forecasting
- URL: http://arxiv.org/abs/2202.13418v2
- Date: Wed, 2 Mar 2022 22:17:56 GMT
- Title: Taming the Long Tail of Deep Probabilistic Forecasting
- Authors: Jedrzej Kozerawski, Mayank Sharan, Rose Yu
- Abstract summary: We identify a long tail behavior in the performance of state-of-the-art deep learning methods on probabilistic forecasting.
We present two moment-based tailedness measurement concepts to improve performance on the difficult tail examples.
We demonstrate the performance of our approach on several real-world datasets including time series andtemporal trajectories.
- Score: 16.136753801449263
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep probabilistic forecasting is gaining attention in numerous applications
ranging from weather prognosis, through electricity consumption estimation, to
autonomous vehicle trajectory prediction. However, existing approaches focus on
improvements on the most common scenarios without addressing the performance on
rare and difficult cases. In this work, we identify a long tail behavior in the
performance of state-of-the-art deep learning methods on probabilistic
forecasting. We present two moment-based tailedness measurement concepts to
improve performance on the difficult tail examples: Pareto Loss and Kurtosis
Loss. Kurtosis loss is a symmetric measurement as the fourth moment about the
mean of the loss distribution. Pareto loss is asymmetric measuring right
tailedness, modeling the loss using a generalized Pareto distribution (GPD). We
demonstrate the performance of our approach on several real-world datasets
including time series and spatiotemporal trajectories, achieving significant
improvements on the tail examples.
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