mlsanityData to 7 Oct 2026

A Multi-Horizon Quantile Recurrent Forecaster

arXiv 1711.11053 ↗First posted 29 Nov 2017stat.MLcited by 211

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Abstract (arXiv)

We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, *forking-sequences*, is designed for sequential nets to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and cold-starts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on Amazon.com, and in a public probabilistic forecasting competition to predict electricity price and load.

Key facts. Cited by 211 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Nov 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 33; 2022: 38; 2023: 51; 2024: 34; 2025: 38; 2026: 17.

211 papers.

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  1. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  2. 2024Chronos: Learning the Language of Time Seriescited by 712
  3. 2023A decoder-only foundation model for time-series forecastingcited by 650
  4. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
  5. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  6. 2021Monash Time Series Forecasting Archivecited by 256
  7. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
  8. 2025Sundial: A Family of Highly Capable Time Series Foundation Modelscited by 168
  9. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  10. 2022CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecastingcited by 143
  11. 2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 102
  12. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 102
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  1. 2024Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecastingcited by 102
  2. 2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
  3. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  4. 2022Sequential Predictive Conformal Inference for Time Seriescited by 71
  5. 2021Domain Adaptation for Time Series Forecasting via Attention Sharingcited by 68
  6. 2021Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxcited by 63
  7. 2025Moirai 2.0: When Less Is More for Time Series Forecastingcited by 59
  8. 2023CARD: Channel Aligned Robust Blend Transformer for Time Series Forecastingcited by 55
  9. 2023SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecastingcited by 53
  10. 2021Quantifying Uncertainty in Deep Spatiotemporal Forecastingcited by 48
  11. 2022Criteria for Classifying Forecasting Methodscited by 48
  12. 2021ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Modelscited by 42
  13. 2023Multi-resolution Time-Series Transformer for Long-term Forecastingcited by 41
  14. 2023AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecastingcited by 36
  15. 2023ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizonscited by 35
  16. 2021Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecastingcited by 32
  17. 2021Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecastingcited by 32
  18. 2024Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfercited by 31
  19. 2021Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecastingcited by 29
  20. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  21. 2022Conformal Prediction with Temporal Quantile Adjustmentscited by 25
  22. 2024CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variablescited by 25
  23. 2021Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Seriescited by 24
  24. 2023Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domaincited by 23
  25. 2022Deep Inventory Managementcited by 22
  26. 2024RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Taskscited by 22
  27. 2021Long Horizon Forecasting With Temporal Point Processescited by 20
  28. 2022Multivariate Time Series Forecasting with Latent Graph Inferencecited by 20
  29. 2022Multivariate Quantile Function Forecastercited by 19
  30. 2025Can Slow-thinking LLMs Reason Over Time? Empirical Studies in Time Series Forecastingcited by 19
  31. 2021Adjusting for Autocorrelated Errors in Neural Networks for Time Seriescited by 18
  32. 2022Interpretable Mixture of Expertscited by 17
  33. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
  34. 2021Probabilistic Hierarchical Forecasting with Deep Poisson Mixturescited by 16
  35. 2021Autoregressive Quantile Flows for Predictive Uncertainty Estimationcited by 15
  36. 2022Robust Probabilistic Time Series Forecastingcited by 15
  37. 2022FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecastingcited by 15
  38. 2021Topological Attention for Time Series Forecastingcited by 14

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