Causal and generative time series: research map

615 accepted papers on Causal and generative time series in Time series, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 2 clusters and 8 approaches. The busiest year so far is 2026.

Within Time series, its share shrank from 40.9% in 2023–24 to 29.1% in 2025–26 (155 → 229 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).

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Approaches and key papers

conformal · causal discovery · predictions · 449 papers

Approaches in this cluster:

Probabilistic and conformal forecasting (121 papers)
Produce calibrated probabilistic or conformal forecasts for series, including hierarchical and non-stationary data.
State-space and latent dynamics forecasting (94 papers)
Forecast via deep state-space, latent-state and dynamics-based models with approximation theory.
Dynamical system reconstruction (90 papers)
Learn latent nonlinear or linear dynamical systems from time series data.
Robust forecasting and evaluation (91 papers)
Study robustness, benchmarking pitfalls and task-based optimization in time series forecasting.
Generative temporal point processes (53 papers)
Uses diffusion, intensity-free and meta-learning methods for temporal point processes.

Most cited and most cited since 2024:

Time-series Generative Adversarial Networks (NeurIPS 2019 · 511 citations)
Deep State Space Models for Time Series Forecasting (NeurIPS 2018 · 429 citations)
CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series (AAAI 2024 · 37 citations)
Root Cause Analysis in Microservice Using Neural Granger Causal Discovery (AAAI 2024 · 33 citations)

differential · continuous · equations · 166 papers