========== Algorithms ========== To learn how to apply imputation within the library, please refer to the `tutorial `_. .. list-table:: :header-rows: 1 * - **Algorithm** - **Family** - **Venue -- Year** * - NuwaTS [33]_ - LLMs - Arxiv -- 2024 * - GPT4TS [34]_ - LLMs - NeurIPS -- 2023 * - 🚧 MOMENT [37]_ - LLMs - ICLR -- 2025 * - - - * - - - * - MissNet [27]_ - Deep Learning - KDD -- 2024 * - MPIN [25]_ - Deep Learning - PVLDB -- 2024 * - BitGraph [32]_ - Deep Learning - ICLR -- 2024 * - BayOTIDE [30]_ - Deep Learning - PMLR -- 2024 * - TimesNet [36]_ - Deep Learning - ICLR -- 2023 * - SAITS [39]_ - Deep Learning - ESWA -- 2023 * - PRISTI [26]_ - Deep Learning - ICDE -- 2023 * - GRIN [29]_ - Deep Learning - ICLR -- 2022 * - CSDI [35]_ - Deep Learning - NeurIPS -- 2021 * - DeepMVI [24]_ - Deep Learning - PVLDB -- 2021 * - HKMFT [31]_ - Deep Learning - TKDE -- 2021 * - MRNN [22]_ - Deep Learning - IEEE Trans on BE -- 2019 * - BRITS [23]_ - Deep Learning - NeurIPS -- 2018 * - GAIN [28]_ - Deep Learning - ICML -- 2018 * - 🚧 SSGAN [38]_ - Deep Learning - AAAI -- 2021 * - 🚧 GP-VAE [40]_ - Deep Learning - AISTATS -- 2020 * - 🚧 NAOMI [41]_ - Deep Learning - NeurIPS -- 2019 * - - - * - - - * - CDRec [1]_ - Matrix Completion - KAIS -- 2020 * - TRMF [8]_ - Matrix Completion - NeurIPS -- 2016 * - GROUSE [3]_ - Matrix Completion - PMLR -- 2016 * - ROSL [4]_ - Matrix Completion - CVPR -- 2014 * - SoftImpute [6]_ - Matrix Completion - JMLR -- 2010 * - SVT [7]_ - Matrix Completion - SIAM J. OPTIM -- 2010 * - SPIRIT [5]_ - Matrix Completion - VLDB -- 2005 * - IterativeSVD [2]_ - Matrix Completion - BIOINFORMATICS -- 2001 * - - - * - - - * - TKCM [11]_ - Pattern Search - EDBT -- 2017 * - STMVL [9]_ - Pattern Search - IJCAI -- 2016 * - DynaMMo [10]_ - Pattern Search - KDD -- 2009 * - - - * - - - * - IIM [12]_ - Machine Learning - ICDE -- 2019 * - XGBOOST [13]_ - Machine Learning - KDD -- 2016 * - MICE [14]_ - Machine Learning - Statistical Software -- 2011 * - MissForest [15]_ - Machine Learning - BioInformatics -- 2011 * - - - * - - - * - KNNImpute - Statistics - _ * - Interpolation - Statistics - _ * - MinImpute - Statistics - _ * - ZeroImpute - Statistics - _ * - MeanImpute - Statistics - _ * - MeanImputeBySeries - Statistics - _ .. list-table:: :header-rows: 1 .. raw:: html

.. _references: References ---------- .. [1] Mourad Khayati, Philippe Cudré-Mauroux, Michael H. Böhlen: Scalable recovery of missing blocks in time series with high and low cross-correlations. Knowl. Inf. Syst. 62(6): 2257-2280 (2020) .. [2] Olga G. Troyanskaya, Michael N. Cantor, Gavin Sherlock, Patrick O. Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman: Missing value estimation methods for DNA microarrays. Bioinform. 17(6): 520-525 (2001) .. [3] Dejiao Zhang, Laura Balzano: Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation. AISTATS 2016: 1460-1468 .. [4] Xianbiao Shu, Fatih Porikli, Narendra Ahuja: Robust Orthonormal Subspace Learning: Efficient Recovery of Corrupted Low-Rank Matrices. CVPR 2014: 3874-3881 .. [5] Spiros Papadimitriou, Jimeng Sun, Christos Faloutsos: Streaming Pattern Discovery in Multiple Time-Series. VLDB 2005: 697-708 .. [6] Rahul Mazumder, Trevor Hastie, Robert Tibshirani: Spectral Regularization Algorithms for Learning Large Incomplete Matrices. J. Mach. Learn. Res. 11: 2287-2322 (2010) .. [7] Jian-Feng Cai, Emmanuel J. Candès, Zuowei Shen: A Singular Value Thresholding Algorithm for Matrix Completion. SIAM J. Optim. 20(4): 1956-1982 (2010) .. [8] Hsiang-Fu Yu, Nikhil Rao, Inderjit S. Dhillon: Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction. NeurIPS 2016: 847-855 .. [9] Xiuwen Yi, Yu Zheng, Junbo Zhang, Tianrui Li: ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data. IJCAI 2016: 2704-2710 .. [10] Lei Li, James McCann, Nancy S. Pollard, Christos Faloutsos: DynaMMo: mining and summarization of coevolving sequences with missing values. 507-516 .. [11] Kevin Wellenzohn, Michael H. Böhlen, Anton Dignös, Johann Gamper, Hannes Mitterer: Continuous Imputation of Missing Values in Streams of Pattern-Determining Time Series. EDBT 2017: 330-341 .. [12] Aoqian Zhang, Shaoxu Song, Yu Sun, Jianmin Wang: Learning Individual Models for Imputation. ICDE (2019) .. [13] Tianqi Chen, Carlos Guestrin: XGBoost: A Scalable Tree Boosting System. KDD 2016: 785-794 .. [14] Royston Patrick , White Ian R.: Multiple Imputation by Chained Equations (MICE): Implementation in Stata. Journal of Statistical Software 2010: 45(4), 1–20. .. [15] Daniel J. Stekhoven, Peter Bühlmann: MissForest - non-parametric missing value imputation for mixed-type data. Bioinform. 28(1): 112-118 (2012) .. [22] Jinsung Yoon, William R. Zame, Mihaela van der Schaar: Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks. IEEE Trans. Biomed. Eng. 66(5): 1477-1490 (2019) .. [23] Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, Yitan Li: BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018: 6776-6786 .. [24] Parikshit Bansal, Prathamesh Deshpande, Sunita Sarawagi: Missing Value Imputation on Multidimensional Time Series. Proc. VLDB Endow. 14(11): 2533-2545 (2021) .. [25] Xiao Li, Huan Li, Hua Lu, Christian S. Jensen, Varun Pandey, Volker Markl: Missing Value Imputation for Multi-attribute Sensor Data Streams via Message Propagation (Extended Version). CoRR abs/2311.07344 (2023) .. [26] Mingzhe Liu, Han Huang, Hao Feng, Leilei Sun, Bowen Du, Yanjie Fu: PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation. ICDE 2023: 1927-1939 .. [27] Kohei Obata, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai: Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series. KDD 2024: 2296-2306 .. [28] Jinsung Yoon, James Jordon, Mihaela van der Schaar: GAIN: Missing Data Imputation using Generative Adversarial Nets. ICML 2018: 5675-5684 .. [29] Andrea Cini, Ivan Marisca, Cesare Alippi: Multivariate Time Series Imputation by Graph Neural Networks. CoRR abs/2108.00298 (2021) .. [30] Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun: BayOTIDE: Bayesian Online Multivariate Time Series Imputation with Functional Decomposition. ICML 2024 .. [31] Liang Wang, Simeng Wu, Tianheng Wu, Xianping Tao, Jian Lu: HKMF-T: Recover From Blackouts in Tagged Time Series With Hankel Matrix Factorization. IEEE Trans. Knowl. Data Eng. 33(11): 3582-3593 (2021) .. [32] Xiaodan Chen, Xiucheng Li, Bo Liu, Zhijun Li: Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values. ICLR 2024 .. [33] Jinguo Cheng, Chunwei Yang, Wanlin Cai, Yuxuan Liang, Qingsong Wen, Yuankai Wu: NuwaTS: a Foundation Model Mending Every Incomplete Time Series. Arxiv 2024 .. [34] Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin: One fits all: power general time series analysis by pretrained LM. NeurIPS 2023 .. [35] Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon: CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation. NeurIPS 2021 .. [36] Haixu Wu, Tengge Hu, Yong Liu, Hang Zhou, Jianmin Wang, Mingsheng Long: TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. ICLR 2023 .. [37] Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, Artur Dubrawski: MOMENT: A Family of Open Time-series Foundation Models. ICLR 2025 .. [38] Xiaoye Miao, Yangyang Wu, Jun Wang, Yunjun Gao, Xudong Mao, Jianwei Yin : Generative Semi-supervised Learning for Multivariate Time Series Imputation. AAAI 2021 .. [39] Wenjie Du, David Côté, Yan Liu : SAITS: : Self-attention-based imputation for time series . ESWA 2023 .. [40] Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt : GP-VAE: Deep Probabilistic Multivariate Time Series Imputation. AISTATS 2020 .. [41] Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue : NAOMI: Non-Autoregressive Multiresolution Sequence Imputation. NeurIPS 2019 .. raw:: html

🚧 = under integration .. raw:: html