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Probabilistic Time Series Forecasting with Implicit Quantile Networks

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arxiv 2107.03743 v1 pith:OXVP3LSR submitted 2021-07-08 cs.LG cs.AI

Adèle Gouttes , Kashif Rasul , Mateusz Koren , Johannes Stephan , Tofigh Naghibi This is my paper · ORCID
classification cs.LGcs.AI
keywords forecastingprobabilisticimplicitnetworksneuralquantileseriestemporal
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Here, we propose a general method for probabilistic time series forecasting. We combine an autoregressive recurrent neural network to model temporal dynamics with Implicit Quantile Networks to learn a large class of distributions over a time-series target. When compared to other probabilistic neural forecasting models on real- and simulated data, our approach is favorable in terms of point-wise prediction accuracy as well as on estimating the underlying temporal distribution.

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Cited by 1 Pith paper

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  1. Relational Conformal Prediction for Correlated Time Series

    cs.LG 2025-02 conditional novelty 6.0 of 10

    CoRel trains a graph neural network on prediction residuals to estimate quantile intervals for correlated time series, reporting narrower intervals than per-series conformal baselines on three benchmarks.

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