# Probabilistic Time Series Forecasting with Implicit Quantile Networks

Source: [Probabilistic Time Series Forecasting with Implicit Quantile Networks](<https://www.sota2.com/research/paper/68fa6a5a1e209131cea27acb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:23:37.152Z

Authors: Ad\\\`ele Gouttes, Kashif Rasul, Mateusz Koren, Johannes Stephan, Tofigh Naghibi

Published: 2021-07-08T10:37:24.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Probabilistic Forecasting on traff](<https://www.sota2.com/research/sota/probabilistic-forecasting-on-traff.md>): Probabilistic Forecasting; traff; ND%: 16.8.

- [Probabilistic Forecasting on elec](<https://www.sota2.com/research/sota/probabilistic-forecasting-on-elec.md>): Probabilistic Forecasting; Elec; ND%: 7.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.03743>)

[PDF](<https://arxiv.org/pdf/2107.03743.pdf>)