REVIEW 6 cited by
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 6 Pith papers
-
Microlensing Detection and Inference via Learned Bayes Factors
A unified transformer-based pipeline detects 99.9% of recoverable simulated microlensing events and outperforms literature hard cuts in the short-duration finite-source regime with amortized neural posterior inference.
-
Flexible Gravitational-Wave Parameter Estimation with Transformers
Dingo-T1 is one transformer model that adapts at inference to arbitrary detector subsets and frequency cuts for gravitational-wave parameter estimation.
-
multivariateGPT: a decoder-only transformer for multivariate categorical and numeric data
multivariateGPT extends next-token prediction to jointly predict the class and continuous value of mixed categorical and numeric time series, with Gaussian uncertainty, and outperforms discrete-token baselines on clin...
-
HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecasting
HyperIMTS, a hypergraph neural network with temporal and variable hyperedges, achieves the lowest forecast MSE among 27 baselines on five irregular multivariate time series datasets.
-
Causal Discovery on Irregular Time Series
A time-window adaptation of PCMCI+ recovers causal graphs on irregularly sampled synthetic events better than fixed-lag PCMCI+.
-
Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification
A systematic review of GAN-based longitudinal data imputation that categorizes methods and shows that most ignore missingness mechanisms, static features, and mixed data types.
Discussion (0). Continue with ORCID to comment.