# Probabilistic Time Series Forecasting with Implicit Quantile Networks

arXiv: https://arxiv.org/abs/2107.03743 · HTML: https://mlsanity.com/paper/2107.03743/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Implicit Quantile Networks for Distributional Reinforcement Learning](https://mlsanity.com/paper/1806.06923/) (2018)
- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://mlsanity.com/paper/1910.03002/) (2019)
- [Fully Parameterized Quantile Function for Distributional Reinforcement Learning](https://mlsanity.com/paper/1911.02140/) (2019)
- [Deep and Confident Prediction for Time Series at Uber](https://mlsanity.com/paper/1709.01907/) (2017)
- [Autoregressive Quantile Networks for Generative Modeling](https://mlsanity.com/paper/1806.05575/) (2018)

## Built on by (most cited first, 17 of 17)

- [Multivariate Quantile Function Forecaster](https://mlsanity.com/paper/2202.11316/) (2022)
- [Deep Learning based Forecasting: a case study from the online fashion industry](https://mlsanity.com/paper/2305.14406/) (2023)
- [Relational Conformal Prediction for Correlated Time Series](https://mlsanity.com/paper/2502.09443/) (2025)
- [SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting](https://mlsanity.com/paper/2312.14880/) (2023)
- [Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks](https://mlsanity.com/paper/2501.07601/) (2025)
- [VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2205.15894/) (2022)
- [Learning to Simulate: Generative Metamodeling via Quantile Regression](https://mlsanity.com/paper/2311.17797/) (2023)
- [Training Implicit Generative Models via an Invariant Statistical Loss](https://mlsanity.com/paper/2402.16435/) (2024)
- [Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand](https://mlsanity.com/paper/2404.17451/) (2024)
- [Deep Generative Quantile Bayes](https://mlsanity.com/paper/2410.08378/) (2024)
- [Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2303.08909/) (2023)
- [C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting](https://mlsanity.com/paper/2312.15002/) (2023)
- [tsGT: Stochastic Time Series Modeling With Transformer](https://mlsanity.com/paper/2403.05713/) (2024)
- [VAEneu: A New Avenue for VAE Application on Probabilistic Forecasting](https://mlsanity.com/paper/2405.04252/) (2024)
- [Task-based Loss Functions in Computer Vision: A Comprehensive Review](https://mlsanity.com/paper/2504.04242/) (2025)
- [Auxiliary Quantile Forecasting with Linear Networks](https://mlsanity.com/paper/2212.02578/) (2022)
- [Stabilizing distribution-free probabilistic forecasts](https://mlsanity.com/paper/2605.28531/) (2026)

## Abstract (arXiv)

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.