Imputation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

12 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 479 papers with code (1,237 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• GAIN: Missing Data Imputation using Generative Adversarial Nets
  7 Jun 2018 8 repositories listed Syntology ran 2 of 8 samples · 6 unverified · 1 pointer-only (licence)
  Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN).
• Recurrent Neural Networks for Multivariate Time Series with Missing Values
  6 Jun 2016 7 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 2 pointer-only (licence)
  Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values.
• Deep Learning in Single-Cell Analysis
  22 Oct 2022 6 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 1 pointer-only (licence)
  Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
• A Transformer-based Framework for Multivariate Time Series Representation Learning
  6 Oct 2020 6 repositories listed Syntology ran 5 of 32 samples · 27 unverified · 2 pointer-only (licence)
  In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series.
• Unsupervised Data Imputation via Variational Inference of Deep Subspaces
  8 Mar 2019 6 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.
• Deep Learning for Multivariate Time Series Imputation: A Survey
  6 Feb 2024 5 repositories listed
  This survey aims to serve as a valuable resource for researchers and practitioners in the field of time series analysis and missing data imputation tasks.
• Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
  18 Sep 2023 5 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Through empirical evaluation across the benchmark, we demonstrate that our approach outperforms deep-learning generation methods in data generation tasks and remains competitive in data imputation.
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
  30 May 2023 5 repositories listed Syntology ran 0 of 21 samples · 21 unverified
  PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series, i.
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
  7 Jul 2021 5 repositories listed Syntology ran 10 of 19 samples · 9 unverified · 1 pointer-only (licence)
  In this paper, we propose Conditional Score-based Diffusion models for Imputation (CSDI), a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data.
• BRITS: Bidirectional Recurrent Imputation for Time Series
  27 May 2018 5 repositories listed Syntology ran 2 of 4 samples · 2 unverified
  It is ubiquitous that time series contains many missing values.
• TSI-Bench: Benchmarking Time Series Imputation
  18 Jun 2024 4 repositories listed
  Despite the development of numerous deep learning algorithms for time series imputation, the community lacks standardized and comprehensive benchmark platforms to effectively evaluate imputation performance across…
• Unity by Diversity: Improved Representation Learning in Multimodal VAEs
  8 Mar 2024 4 repositories listed Syntology ran 8 of 9 samples · 1 unverified · 4 pointer-only (licence)
  Such architectures impose hard constraints on the model.
• GP-VAE: Deep Probabilistic Time Series Imputation
  9 Jul 2019 4 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years.
• Imaging Time-Series to Improve Classification and Imputation
  1 Jun 2015 4 repositories listed
  We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images.
• TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
  5 Oct 2022 3 repositories listed
  TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block.
• SAITS: Self-Attention-based Imputation for Time Series
  17 Feb 2022 3 repositories listed Syntology ran 3 of 10 samples · 7 unverified
  Missing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis.
• Geometry- and Accuracy-Preserving Random Forest Proximities
  29 Jan 2022 3 repositories listed
  Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning.
• A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
  23 Oct 2020 3 repositories listed
  Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management.
• Lung Segmentation from Chest X-rays using Variational Data Imputation
  20 May 2020 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19).
• Bayesian Temporal Factorization for Multidimensional Time Series Prediction
  14 Oct 2019 3 repositories listed
  In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series -- in particular spatiotemporal data -- in the presence of missing values.
• Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
  17 May 2019 3 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, such approaches typically rely on…
• On the consistency of supervised learning with missing values
  19 Feb 2019 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  A striking result is that the widely-used method of imputing with a constant, such as the mean prior to learning is consistent when missing values are not informative.
• Handling Incomplete Heterogeneous Data using VAEs
  10 Jul 2018 3 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data.
• Variational Autoencoder with Arbitrary Conditioning
  6 Jun 2018 3 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot".
• MIDA: Multiple Imputation using Denoising Autoencoders
  8 May 2017 3 repositories listed
  Missing data is a significant problem impacting all domains.
• Input Convex Neural Networks
  22 Sep 2016 3 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  We show that many existing neural network architectures can be made input-convex with a minor modification, and develop specialized optimization algorithms tailored to this setting.
• PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
  16 Jun 2025 2 repositories listed
  Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events.
• JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs
  17 Feb 2025 2 repositories listed Syntology ran 0 of 19 samples · 19 unverified
  We introduce a simple method for probabilistic predictions on tabular data based on Large Language Models (LLMs) called JoLT (Joint LLM Process for Tabular data).
• TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
  21 Jan 2025 2 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications.
• Using matrix-product states for time-series machine learning
  20 Dec 2024 2 repositories listed
  Here, we develop and apply an MPS-based algorithm, MPSTime, for learning a joint probability distribution underlying an observed time-series dataset, and show how it can be used to tackle important time-series ML…

Syntology lines on 20 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.