# A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series

arXiv: https://arxiv.org/abs/2012.00168 · HTML: https://mlsanity.com/paper/2012.00168/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 30 of 42)

- [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://mlsanity.com/paper/2110.05357/) (2021)
- [Modeling Irregular Time Series with Continuous Recurrent Units](https://mlsanity.com/paper/2111.11344/) (2021)
- [Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2306.10125/) (2023)
- [All-in-one simulation-based inference](https://mlsanity.com/paper/2404.09636/) (2024)
- [Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking](https://mlsanity.com/paper/2302.10902/) (2023)
- [Universal Time-Series Representation Learning: A Survey](https://mlsanity.com/paper/2401.03717/) (2024)
- [TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series](https://mlsanity.com/paper/2310.01327/) (2023)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis](https://mlsanity.com/paper/2510.01538/) (2025)
- [Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2308.16818/) (2023)
- [DuETT: Dual Event Time Transformer for Electronic Health Records](https://mlsanity.com/paper/2304.13017/) (2023)
- [Event-Based Contrastive Learning for Medical Time Series](https://mlsanity.com/paper/2312.10308/) (2023)
- [Can Multimodal LLMs Perform Time Series Anomaly Detection?](https://mlsanity.com/paper/2502.17812/) (2025)
- [Stop&Hop: Early Classification of Irregular Time Series](https://mlsanity.com/paper/2208.09795/) (2022)
- [Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series](https://mlsanity.com/paper/2408.08328/) (2024)
- [ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting](https://mlsanity.com/paper/2509.23313/) (2025)
- [LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values](https://mlsanity.com/paper/2109.14844/) (2021)
- [IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers](https://mlsanity.com/paper/2305.06741/) (2023)
- [Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey](https://mlsanity.com/paper/2308.04947/) (2023)
- [PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series](https://mlsanity.com/paper/2308.13703/) (2023)
- [MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.10164/) (2023)
- [IMTS is Worth Time × Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction](https://mlsanity.com/paper/2505.22815/) (2025)
- [MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification](https://mlsanity.com/paper/2509.17809/) (2025)
- [The hidden risks of temporal resampling in clinical reinforcement learning](https://mlsanity.com/paper/2602.06603/) (2026)
- [MILM: Large Language Models for Multimodal Irregular Time Series with Informative Sampling](https://mlsanity.com/paper/2605.13711/) (2026)
- [Heteroscedastic Temporal Variational Autoencoder For Irregular Time Series](https://mlsanity.com/paper/2107.11350/) (2021)
- [Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling](https://mlsanity.com/paper/2502.15785/) (2025)
- [multivariateGPT: a decoder-only transformer for multivariate categorical and numeric data](https://mlsanity.com/paper/2505.21680/) (2025)
- [Beyond Observations: Reconstruction Error-Guided Irregularly Sampled Time Series Representation Learning](https://mlsanity.com/paper/2511.06854/) (2025)
- [A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarios](https://mlsanity.com/paper/2512.23748/) (2025)

## Abstract (arXiv)

Irregularly sampled time series data arise naturally in many application domains including biology, ecology, climate science, astronomy, and health. Such data represent fundamental challenges to many classical models from machine learning and statistics due to the presence of non-uniform intervals between observations. However, there has been significant progress within the machine learning community over the last decade on developing specialized models and architectures for learning from irregularly sampled univariate and multivariate time series data. In this survey, we first describe several axes along which approaches to learning from irregularly sampled time series differ including what data representations they are based on, what modeling primitives they leverage to deal with the fundamental problem of irregular sampling, and what inference tasks they are designed to perform. We then survey the recent literature organized primarily along the axis of modeling primitives. We describe approaches based on temporal discretization, interpolation, recurrence, attention and structural invariance. We discuss similarities and differences between approaches and highlight primary strengths and weaknesses.