mlsanityData to 14 Oct 2026

ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons

arXiv 2310.07446 ↗First posted 11 Oct 2023cs.LGcited by 36

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Abstract (arXiv)

Delivering precise point and distributional forecasts across a spectrum of prediction horizons represents a significant and enduring challenge in the application of time-series forecasting within various industries. Prior research on developing deep learning models for time-series forecasting has often concentrated on isolated aspects, such as long-term point forecasting or short-term probabilistic estimations. This narrow focus may result in skewed methodological choices and hinder the adaptability of these models to uncharted scenarios. While there is a rising trend in developing universal forecasting models, a thorough understanding of their advantages and drawbacks, especially regarding essential forecasting needs like point and distributional forecasts across short and long horizons, is still lacking. In this paper, we present ProbTS, a benchmark tool designed as a unified platform to evaluate these fundamental forecasting needs and to conduct a rigorous comparative analysis of numerous cutting-edge studies from recent years. We dissect the distinctive data characteristics arising from disparate forecasting requirements and elucidate how these characteristics can skew methodological preferences in typical research trajectories, which often fail to fully accommodate essential forecasting needs. Building on this, we examine the latest models for universal time-series forecasting and discover that our analyses of methodological strengths and weaknesses are also applicable to these universal models. Finally, we outline the limitations inherent in current research and underscore several avenues for future exploration.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Builds on 29 of its 71 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

29 of 71 references.

References · 29

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17 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 12; 2026: 24.

36 papers.

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  1. 2025fev-bench: A Realistic Benchmark for Time Series Forecastingcited by 64
  2. 2026It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarkscited by 33
  3. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 29
  4. 2025From Samples to Scenarios: A New Paradigm for Probabilistic Forecastingcited by 10
  5. 2026Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecastingcited by 9
  6. 2025K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecastingcited by 8
  7. 2025TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectivenesscited by 7
  8. 2026TempusBench: An Evaluation Framework for Time-Series Forecastingcited by 4
  9. 2025SynTSBench: Rethinking Temporal Pattern Learning in Deep Learning Models for Time Seriescited by 3
  10. 2025FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecastingcited by 3
  11. 2026Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertaintycited by 3
  12. 2025TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAEcited by 2
24 more
  1. 2026FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Financecited by 2
  2. 2026Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Contextcited by 2
  3. 2025Non-collective Calibrating Strategy for Time Series Forecastingcited by 1
  4. 2025ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecastingcited by 1
  5. 2025Time Series Foundation Models for Process Model Forecastingcited by 1
  6. 2026Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Poolscited by 1
  7. 2026Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependencecited by 1
  8. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applicationscited by 1
  9. 2025BinConv: A Neural Architecture for Ordinal Encoding in Time-Series Forecastingcited by 0
  10. 2026TimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalizationcited by 0
  11. 2026Data-driven Lake Water Quality Forecasting for Time Series with Missing Data using Machine Learningcited by 0
  12. 2026Signature-Kernel Based Evaluation Metrics for Robust Probabilistic and Tail-Event Forecastingcited by 0
  13. 2026U-Former ODE: Fast Probabilistic Forecasting of Irregular Time Seriescited by 0
  14. 2026Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecastingcited by 0
  15. 2026GenTS: A Comprehensive Benchmark Library for Generative Time Series Modelscited by 0
  16. 2026TS-Fault: Benchmarking Time Series Forecasters Against Structural Faultscited by 0
  17. 2026Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecastingcited by 0
  18. 2026CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecastingcited by 0
  19. 2026DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecastingcited by 0
  20. 2026Principled Koopman Representations with Kalman Inference for Efficient Time-Series Predictioncited by 0
  21. 2026Beyond Average Error through Oracle-Informed Stress Tests for Time-Series Forecastingcited by 0
  22. 2026STRIDE: State-Transition Representation via Increment Dynamics and Evolutioncited by 0
  23. 2026XMatch: Enhancing Covariate-Aware Time Series Forecasting through Tree-Structured Exogenous Matchingcited by 0
  24. 2026Time Series Forecasting Benchmarks Need Scenario-Grounded Stress Testingcited by 0