Probabilistic Time Series Forecasting with Implicit Quantile Networks

• URL: http://arxiv.org/abs/2107.03743v1
• Date: Thu, 8 Jul 2021 10:37:24 GMT
• Title: Probabilistic Time Series Forecasting with Implicit Quantile Networks
• Authors: Ad\`ele Gouttes, Kashif Rasul, Mateusz Koren, Johannes Stephan, Tofigh Naghibi
• Abstract summary: 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. Our approach is favorable in terms of point-wise prediction accuracy as well as on estimating the underlying temporal distribution.
• Score: 0.7249731529275341
• License: http://creativecommons.org/licenses/by/4.0/
• 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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