========== Algorithms ========== All algorithms developed in ImputeGAP are available in the ``ts.algorithms`` module. .. list-table:: :header-rows: 1 * - **Family** - **Algorithm** - **Venue -- Year** * - Matrix Completion - CDRec [1]_ - KAIS -- 2020 * - Matrix Completion - TRMF [8]_ - NeurIPS -- 2016 * - Matrix Completion - GROUSE [3]_ - PMLR -- 2016 * - Matrix Completion - ROSL [4]_ - CVPR -- 2014 * - Matrix Completion - SoftImpute [6]_ - JMLR -- 2010 * - Matrix Completion - SVT [7]_ - SIAM J. OPTIM -- 2010 * - Matrix Completion - SPIRIT [5]_ - VLDB -- 2005 * - Matrix Completion - IterativeSVD [2]_ - BIOINFORMATICS -- 2001 * - Pattern Search - TKCM [11]_ - EDBT -- 2017 * - Pattern Search - ST-MVL [9]_ - IJCAI -- 2016 * - Pattern Search - DynaMMo [10]_ - KDD -- 2009 * - Machine Learning - IIM [12]_ - ICDE -- 2019 * - Machine Learning - XGBI [13]_ - KDD -- 2016 * - Machine Learning - Mice [14]_ - Statistical Software -- 2011 * - Machine Learning - MissForest [15]_ - BioInformatics -- 2011 * - Deep Learning - MPIN [25]_ - PVLDB -- 2024 * - Deep Learning - MissNet [27]_ - KDD -- 2024 * - Deep Learning - BITGraph [32]_ - ICLR -- 2024 * - Deep Learning - BayOTIDE [30]_ - PMLR -- 2024 * - Deep Learning - PriSTI [26]_ - ICDE -- 2023 * - Deep Learning - GRIN [29]_ - ICLR -- 2022 * - Deep Learning - DeepMVI [24]_ - PVLDB -- 2021 * - Deep Learning - HKMF-T [31]_ - TKDE -- 2021 * - Deep Learning - MRNN [22]_ - IEEE Trans on BE -- 2019 * - Deep Learning - BRITS [23]_ - NeurIPS -- 2018 * - Deep Learning - GAIN [28]_ - ICML -- 2018 * - Statistics - KNNImpute - _ * - Statistics - Interpolation - _ * - Statistics - MinImpute - _ * - Statistics - ZeroImpute - _ * - Statistics - MeanImpute - _ * - Statistics - MeanImputeBySeries - _ .. _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. NIPS 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 (Technical Report). CoRR abs/2004.03436 (2020) .. [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