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arXiv · 2107.03743

Probabilistic Time Series Forecasting with Implicit Quantile Networks

Adèle Gouttes·Kashif Rasul·Mateusz Koren·Johannes Stephan·Tofigh Naghibi
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Abstract

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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Adèle Gouttes, Kashif Rasul, Mateusz Koren, Johannes Stephan, Tofigh Naghibi. 2021-07-08. Probabilistic Time Series Forecasting with Implicit Quantile Networks. https://arxiv.org/abs/2107.03743

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