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Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios.
Statistical comparisons of classifiers over multiple data sets
Janez Demsar · 2006
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Application of machine learning techniques for supply chain demand forecasting
Réal André Carbonneau, Kevin Laframboise, and Rustam M. Vahidov · 2008
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Extracting and composing robust features with denoising autoencoders
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Dtw-d: time series semi-supervised learning from a single example
Yanping Chen, Bing Hu, Eamonn J. Keogh, and Gustavo E. A. P. A. Batista · 2013
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei A. Efros · 2016
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Time-series extreme event forecasting with neural networks at uber
Nikolay Pavlovich Laptev, Jason Yosinski, Li Erran Li, and Slawek Smyl · 2017
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Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2017
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Modeling long- and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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Multivariate bayesian structural time series model
Jinwen Qiu, Sreenivasa Rao Jammalamadaka, and Ning Ning · 2018
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Attend and diagnose: Clinical time series analysis using attention models
Huan-Zhi Song, Deepta Rajan, Jayaraman J. Thiagarajan, and Andreas Spanias · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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The ucr time series archive
Hoang Anh Dau, A. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ratanamahatana, and Eamonn J. Keogh · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Unsupervised scalable representation learning for multivariate time series
Jean-Yves Franceschi, Aymeric Dieuleveut, and Martin Jaggi · 2019
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Similarity preserving representation learning for time series clustering
Qi Lei, Jinfeng Yi, Roman Vaculín, Lingfei Wu, and Inderjit S. Dhillon · 2019
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Unsupervised representation learning for time series with temporal neighborhood coding
Sana Tonekaboni, Danny Eytan, and Anna Goldenberg · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
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A transformer-based framework for multivariate time series representation learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wan Zhang · 2021
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Time series generation with masked autoencoder
Meng fang Zha · 2022
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Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
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Cdsa: Cross-dimensional self-attention for multivariate, geo-tagged time series imputation
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Pytorch: An imperative style, high-performance deep learning library
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N-beats: Neural basis expansion analysis for interpretable time series forecasting
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Beit: Bert pre-training of image transformers
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An empirical study of training self-supervised vision transformers
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Masked autoencoders as spatiotemporal learners
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Graphmae: Self-supervised masked graph autoencoders
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Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting
Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, and Schahram Dustdar · 2022
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Masked autoencoders for point cloud self-supervised learning
Yatian Pang, Wenxiao Wang, Francis E. H. Tay, W. Liu, Yonghong Tian, and Liuliang Yuan · 2022
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Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting
Zezhi Shao, Zhao Zhang, Fei Wang, and Yongjun Xu · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Ts2vec: Towards universal representation of time series
Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yu Tong, and Bixiong Xu · 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
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