The imputation of missing values in time series has many applications in healthcare and finance. While autoregressive models are natural candidates for time series imputation, score-based diffusion models have recently outperformed existing counterparts including autoregressive models in many tasks such as image generation and audio synthesis, and would be promising for time series imputation. 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. Unlike existing score-based approaches, the conditional diffusion model is explicitly trained for imputation and can exploit correlations between observed values. On healthcare and environmental data, CSDI improves by 40-65% over existing probabilistic imputation methods on popular performance metrics. In addition, deterministic imputation by CSDI reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods. Furthermore, CSDI can also be applied to time series interpolation and probabilistic forecasting, and is competitive with existing baselines. The code is available at https://github.com/ermongroup/CSDI.
Missing data is a common problem faced with real-world datasets. Imputation is a widely used technique to estimate the missing data. State-of-the-art imputation approaches, such as Generative Adversarial Imputation Nets (GAIN), model the distribution of observed data to approximate the missing values. Such an approach usually models a single distribution for the entire dataset, which overlooks the class-specific characteristics of the data. Class-specific characteristics are especially useful when there is a class imbalance. We propose a new method for imputing missing data based on its class-…
Cross-Domain Conditional Diffusion Models for Time Series Imputation
Northwestern University（西北大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Arizona State University（亚利桑那州立大学）；Virginia Tech（弗吉尼亚理工学院）；Squirrel Ai Learning
Beihang University（北京航空航天大学）；University of Central Florida（中佛罗里达大学）
Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in real-world scenarios is usually incomplete due to sensor failures or transmission loss. Spatiotemporal imputation aims to fill the missing values according to the observed values and the underlying spatiotemporal dependence of them. The previous dominant models impute missing values autoregressively and suffer from the problem of error accumulation. As emerging powerful generative models, the diffusion probabilistic…
Georgia Institute of Technology（佐治亚理工学院）；Anonymous University（匿名大学）
Incomplete instances with various missing attributes in many real-world applications have brought challenges to the classification tasks. Missing values imputation methods are often employed to replace the missing values with substitute values. However, this process often separates the imputation and classification, which may lead to inferior performance since label information are often ignored during imputation. Moreover, traditional methods may rely on improper assumptions to initialize the missing values, whereas the unreliability of such initialization might lead to inferior performance. …
Filling the Missings: Spatiotemporal Data Imputation by Conditional Diffusion
School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）；School of Software, Tiangong University（天津理工软件学院）；University of Southern Queensland（南方昆士兰大学）
We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the cornerstone of modern search, recommendation system, and information retrieval applications. Empirical ranking datasets do not always follow standard Gaussian distributions or Missing Completely At Random (MCAR) mechanism, which are standard assumptions of classic missing data imputation methods. Our methodology provides a simple solution that offers compatible imputation guarantees while relaxing assumptions for mis…
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
School of Computer Science, University of Technology Sydney（悉尼科技大学计算机科学学院）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；Department of Computer Science and Engineering, University of California, San Diego（加利福尼亚大学圣地亚哥分校计算机科学与工程学院）
Label-Guided Imputation via Forest-Based Proximities for Improved Time Series Classification
1 Department of Statistics, Brigham Young University, Provo, UT, USA Emails；2 Department of Computer Science, Brigham Young University, Provo, UT, USA Email；3 Department of Public Health, Brigham Young University, Provo, UT, USA Email；4 Department of Neurology, Vagelos College of Physicians \& Surgeons, Columbia University, New York, NY, USA Email；5 Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA Email；6 Elmhurst Hospital Center, NYC Health + Hospitals, New York, NY, USA Email；7 Department of Mathematics \& Statistics, Utah State University, Logan, UT, USA Email
DiffPuter: Empowering Diffusion Models for Missing Data Imputation
Computer Science Department, University of Illinois at Chicago（伊利诺伊大学芝加哥分校计算机科学系）；Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard（MIT和哈佛大学Broad研究所埃里克和文迪·施密特中心）
Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GA…
A diffusion-based generative model for financial time series via geometric Brownian motion
Department of Mathematics, POSTECH（POSTECH数学系）；Department of Financial Mathematics, Gachon University（高丽大学金融数学系）；Department of Applied Artificial Intelligence, SeoulTech（首尔科技大学应用人工智能系）
The University of Tokyo（东京大学）；Preferred Networks（首选网络）
Missing value imputation in machine learning is the task of estimating the missing values in the dataset accurately using available information. In this task, several deep generative modeling methods have been proposed and demonstrated their usefulness, e.g., generative adversarial imputation networks. Recently, diffusion models have gained popularity because of their effectiveness in the generative modeling task in images, texts, audio, etc. To our knowledge, less attention has been paid to the investigation of the effectiveness of diffusion models for missing value imputation in tabular data…
Pretrained-Guided Conditional Diffusion Models for Microbiome Data Analysis
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；College of Nursing Health Sciences, Yunnan Open University（云南开放大学护理与健康科学学院）
GAUDI: Geometry-Aware Diffusion for Calibrated Air-Quality Time-Series Imputation
Columbia University（哥伦比亚大学）；University of California, Davis（加州大学戴维斯分校）；University of California, San Diego（加州大学圣迭戈分校）；Georgia Institute of Technology（佐治亚理工学院）；Lehigh University（理海大学）；Stony Brook University（纽约州立大学石溪分校）；San Francisco State University（旧金山州立大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders
Department of Actuarial Science, Faculty of Business and Economics, University of Lausanne（卢塞恩大学商学院精算科学系）；Institute of Mathematics and Statistics, Faculty of Science and Technology, University of Tartu（塔尔图大学科学与技术学院数学与统计学研究所）
Impute-MACFM: Imputation based on Mask-Aware Flow Matching
Dept. of Graduate Computer Science and Engineering, Yeshiva University（耶鲁大学研究生计算机科学与工程系）；Dept. of Computer and Information Science, University of Massachusetts Dartmouth（马萨诸塞大学达特茅斯分校计算机与信息科学系）；University of Massachusetts Chan Medical School（马萨诸塞大学查普曼医学学校）
本文提出掩码感知条件流匹配框架 Impute-MACFM，通过缺失项轨迹、观测值稳定约束、一致性正则化和投影 ODE 推理，在多种表格基准上实现更鲁棒高效的 SOTA 插补。
Yantai University（烟台大学）；East China Normal University（华东师范大学）
Datasets with missing values are very common in real world applications. GAIN, a recently proposed deep generative model for missing data imputation, has been proved to outperform many state-of-the-art methods. But GAIN only uses a reconstruction loss in the generator to minimize the imputation error of the non-missing part, ignoring the potential category information which can reflect the relationship between samples. In this paper, we propose a novel unsupervised missing data imputation method named PC-GAIN, which utilizes potential category information to further enhance the imputation powe…
Detection and Imputation based Two-Stage Denoising Diffusion Power System Measurement Recovery under Cyber-Physical Uncertainties
State Key Laboratory of Advanced Electromagnetic Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology（华中科技大学电气与电子工程学院先进电磁技术国家重点实验室）；Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University（约克大学拉森德工程学院电气工程与计算机科学系）
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
Conditional expectation with regularization for missing data imputation
University of Science, Ho Chi Minh City（胡志明市科学大学）；Simula Metropolitan（Simula大都会）；University of Notre Dame（圣母大学）；Vietnam National University, Ho Chi Minh City（胡志明市越南国家大学）
MGE-LDM: Joint Latent Diffusion for Simultaneous Music Generation and Source Extraction
Music and Audio Research Group (MARG)（音乐与音频研究组）；Interdisciplinary Program in Artificial Intelligence (IPAI)（人工智能跨学科项目）；AIIS（人工智能研究所）；Department of Intelligence and Information（智能与信息系）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；North Carolina State University（北卡罗来纳州立大学）
Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks. However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$. Traditional conditional approaches provide a model for a fixed set of covariates conditioned on another fixed set of observed covariates. Instead, in this work we develop a model that is cap…
How to learn a good predictor on data with missing values? Most efforts focus on first imputing as well as possible and second learning on the completed data to predict the outcome. Yet, this widespread practice has no theoretical grounding. Here we show that for almost all imputation functions, an impute-then-regress procedure with a powerful learner is Bayes optimal. This result holds for all missing-values mechanisms, in contrast with the classic statistical results that require missing-at-random settings to use imputation in probabilistic modeling. Moreover, it implies that perfect conditi…
International Research Center for Neurointelligence (IRCN)（国际神经智能研究中心）；The University of Tokyo（东京大学）；Graduate School of Engineering, Nagoya Institute of Technology（名古屋技术大学工学院）；Hitachi, Ltd.（日立株式会社）
stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Hefei Institutes of Physical Science, Chinese Academy of Science（中国科学院合肥物质科学研究院）
Missing values widely exist in many real-world datasets, which hinders the performing of advanced data analytics. Properly filling these missing values is crucial but challenging, especially when the missing rate is high. Many approaches have been proposed for missing value imputation (MVI), but they are mostly heuristics-based, lacking a principled foundation and do not perform satisfactorily in practice. In this paper, we propose a probabilistic framework based on deep generative models for MVI. Under this framework, imputing the missing entries amounts to seeking a fixed-point solution betw…
Missing numerical values are prevalent, e.g., owing to unreliable sensor reading, collection and transmission among heterogeneous sources. Unlike categorized data imputation over a limited domain, the numerical values suffer from two issues: (1) sparsity problem, the incomplete tuple may not have sufficient complete neighbors sharing the same/similar values for imputation, owing to the (almost) infinite domain; (2) heterogeneity problem, different tuples may not fit the same (regression) model. In this study, enlightened by the conditional dependencies that hold conditionally over certain tupl…
