ICML 2023 accepted papers on Time series
28 of the 1,865 papers accepted at ICML 2023 (1.5%) are on Time series, compared with 1.1% at ICML 2022. They fall into 4 topics.
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Topics and papers
Causal and generative time series · 20 papers
- Non-autoregressive Conditional Diffusion Models for Time Series Prediction (14 citations)
- Sequential Predictive Conformal Inference for Time Series (11 citations)
- Generalized Teacher Forcing for Learning Chaotic Dynamics (7 citations)
- Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion (7 citations)
- Markovian Gaussian Process Variational Autoencoders (5 citations)
- Probabilistic Imputation for Time-series Classification with Missing Data (5 citations)
- Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation (3 citations)
- Deep Latent State Space Models for Time-Series Generation (2 citations)
- Feature Programming for Multivariate Time Series Prediction (2 citations)
- Sequential Monte Carlo Learning for Time Series Structure Discovery (2 citations)
- Directed Chain Generative Adversarial Networks (1 citation)
- Learning Perturbations to Explain Time Series Predictions (1 citation)
and 8 more in the interactive map.
Foundation models for time series · 6 papers
- Domain Adaptation for Time Series Under Feature and Label Shifts (17 citations)
- Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling (5 citations)
- Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series (1 citation)
- Context Consistency Regularization for Label Sparsity in Time Series
- Regularization-free Diffeomorphic Temporal Alignment Nets
- SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series
Anomaly detection · 1 paper
Long-term forecasting · 1 paper
- Learning Deep Time-index Models for Time Series Forecasting (14 citations)