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arXiv · 1807.09011

Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series

Axel Brando·Jose A. Rodríguez-Serrano·Mauricio Ciprian·Roberto Maestre·Jordi Vitrià
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Abstract

Deep Learning is a consolidated, state-of-the-art Machine Learning tool to fit a function when provided with large data sets of examples. However, in regression tasks, the straightforward application of Deep Learning models provides a point estimate of the target. In addition, the model does not take into account the uncertainty of a prediction. This represents a great limitation for tasks where communicating an erroneous prediction carries a risk. In this paper we tackle a real-world problem of forecasting impending financial expenses and incomings of customers, while displaying predictable monetary amounts on a mobile app. In this context, we investigate if we would obtain an advantage by applying Deep Learning models with a Heteroscedastic model of the variance of a network's output. Experimentally, we achieve a higher accuracy than non-trivial baselines. More importantly, we introduce a mechanism to discard low-confidence predictions, which means that they will not be visible to users. This should help enhance the user experience of our product.

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BibTeXRIS

Axel Brando, Jose A. Rodríguez-Serrano, Mauricio Ciprian, Roberto Maestre, Jordi Vitrià. 2018-07-24. Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series. https://arxiv.org/abs/1807.09011

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