Probabilistic Time Series Forecasting

Benchmarks archive 2025-07-28

3 leaderboard tables shown for this task, 3 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 37 papers with code (63 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
  13 Apr 2017 19 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Probabilistic forecasting, i.
• AA-Forecast: Anomaly-Aware Forecast for Extreme Events
  21 Aug 2022 9 repositories listed
  Moreover, the framework employs a dynamic uncertainty optimization algorithm that reduces the uncertainty of forecasts in an online manner.
• Deep and Confident Prediction for Time Series at Uber
  6 Sep 2017 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing.
• Probabilistic Forecasting with Temporal Convolutional Neural Network
  11 Jun 2019 5 repositories listed Syntology ran 0 of 8 samples · 8 unverified
  We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting.
• AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
  10 Aug 2023 3 repositories listed
  We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting.
• Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
  29 Apr 2022 2 repositories listed
  The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control.
• Deep State Space Models for Time Series Forecasting
  1 Dec 2018 2 repositories listed
  We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning.
• $K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
  29 May 2025 1 repository listed Syntology ran 0 of 2 samples · 2 unverified · 2 pointer-only (licence)
  Probabilistic Time Series Forecasting (PTSF) plays a crucial role in decision-making across various fields, including economics, energy, and transportation.
• Non-stationary Diffusion For Probabilistic Time Series Forecasting
  7 May 2025 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  A diffusion-based probabilistic forecasting framework, termed Non-stationary Diffusion (NsDiff), is designed based on LSNM that is capable of modeling the changing pattern of uncertainty.
• Mamba time series forecasting with uncertainty quantification
  13 Mar 2025 1 repository listed
  We find that in both the electricity consumption and traffic occupancy benchmark, the true trajectory stays within the predicted uncertainty interval at the two-sigma level about 95\% of the time.
• Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
  3 Oct 2024 1 repository listed
  Then, we devise an ad-hoc denoising-based temporal contrastive learning to explicitly amplify the predictive mutual information between past observations and future forecasts.
• Koopman Ensembles for Probabilistic Time Series Forecasting
  11 Mar 2024 1 repository listed
  In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed.
• MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process
  9 Mar 2024 1 repository listed Syntology ran 5 of 10 samples · 5 unverified
  However, the effective utilization of their strong modeling ability in the probabilistic time series forecasting task remains an open question, partially due to the challenge of instability arising from their stochastic…
• Hyperparameter Tuning MLPs for Probabilistic Time Series Forecasting
  7 Mar 2024 1 repository listed
  Time series forecasting attempts to predict future events by analyzing past trends and patterns.
• DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
  21 Feb 2024 1 repository listed
  Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and…
• Multivariate Probabilistic Time Series Forecasting with Correlated Errors
  1 Feb 2024 1 repository listed Syntology ran 3 of 4 samples · 1 unverified
  Accurately modeling the correlation structure of errors is critical for reliable uncertainty quantification in probabilistic time series forecasting.
• Fin-GAN: forecasting and classifying financial time series via generative adversarial networks
  31 Jan 2024 1 repository listed
  We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series.
• MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process Download PDF
  15 Jan 2024 1 repository listed
  To address this challenge, we introduce a novel Multi-Granularity Time Series Diffusion (MG-TSD) model, which achieves state-of-the-art predictive performance by leveraging the inherent granularity levels within the…
• Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
  12 Oct 2023 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.
• Deep Optimal Timing Strategies for Time Series
  9 Oct 2023 1 repository listed
  Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data.
• Residential Load Forecasting: An Online-Offline Deep Kernel Learning Method
  28 Jul 2023 1 repository listed
  Furthermore, to better address the high uncertainty of RLF, a learning scheme combing both offline and online learning is specifically developed for the regressor.
• Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting
  21 Jul 2023 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  Prior works on time series diffusion models have primarily focused on developing conditional models tailored to specific forecasting or imputation tasks.
• Better Batch for Deep Probabilistic Time Series Forecasting
  26 May 2023 1 repository listed Syntology ran 0 of 2 samples · 2 unverified
  Deep probabilistic time series forecasting has gained attention for its ability to provide nonlinear approximation and valuable uncertainty quantification for decision-making.
• Robust Probabilistic Time Series Forecasting
  24 Feb 2022 1 repository listed
  Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties.
• Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting
  17 Feb 2022 1 repository listed Syntology ran 0 of 6 samples · 6 unverified
  EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data.
• Parameter Efficient Deep Probabilistic Forecasting
  6 Dec 2021 1 repository listed
  However, these methods require a large number of parameters to be learned, which imposes high memory requirements on the computational resources for training such models.
• Temporal Convolutional Attention Neural Networks for Time Series Forecasting
  23 Sep 2021 1 repository listed
  TCAN requires less number of convolutional layers than TCNN for an extended receptive field, is faster to train and is able to visualize the most important timesteps for the prediction.
• CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
  15 Sep 2021 1 repository listed Syntology ran 5 of 8 samples · 3 unverified
  We use CAMul for multiple domains with varied sources and modalities and show that CAMul outperforms other state-of-art probabilistic forecasting models by over 25\% in accuracy and calibration.
• Probabilistic Time Series Forecasting with Implicit Quantile Networks
  8 Jul 2021 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Here, we propose a general method for probabilistic time series forecasting.
• ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models
  18 Jun 2021 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  However, many existing works can not be widely used because of the constraints of functional form of generative models or the sensitivity to hyperparameters.

Syntology lines on 14 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.