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Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification.
Signature verification using a” siamese” time delay neural network
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Time series analysis and its applications , volume 3
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Time series shapelets: a new primitive for data mining
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Real time prediction for converter gas tank levels based on multi-output least square support vector regressor
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Time series analysis: forecasting and control
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Unsupervised feature extraction by time-contrastive learning and nonlinear ica
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Mimic-iii, a freely accessible critical care database
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Online arima algorithms for time series prediction
Liu, C., Hoi, S. C., Zhao, P., and Sun, J · 2016
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Wavenet: A generative model for raw audio
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., and Kavukcuoglu, K · 2016
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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A multi-horizon quantile recurrent forecaster
Wen, R., Torkkola, K., Narayanaswamy, B., and Madeka, D · 2017
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Bai, S., Kolter, J. Z., and Koltun, V · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Lai, G., Chang, W.-C., Yang, Y., and Liu, H · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Wu, Z., Xiong, Y., Yu, S. X., and Lin, D · 2018
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Unsupervised scalable representation learning for multivariate time series
Franceschi, J.-Y., Dieuleveut, A., and Jaggi, M · 2019
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Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
Li, S., Jin, X., Xuan, Y., Zhou, X., Chen, W., Wang, Y.-X., and Yan, X · 2019
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Time-series representation learning via temporal and contextual contrasting
Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., and Guan, C · 2021
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Clocs: Contrastive learning of cardiac signals across space, time, and patients
Kiyasseh, D., Zhu, T., and Clifton, D. A · 2021
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Understanding self-supervised learning dynamics without contrastive pairs
Tian, Y., Chen, X., and Ganguli, S · 2021
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Unsupervised representation learning for time series with temporal neighborhood coding
Tonekaboni, S., Eytan, D., and Goldenberg, A · 2021
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Neighborhood contrastive learning applied to online patient moninring
Yèche, H., Dresdner, G., Locatello, F., Hüser, M., and Rätsch, G · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J., and Januschowski, T · 2020
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Financial time series forecasting with deep learning: A systematic literature review: 2005–2019
Sezer, O. B., Gudelek, M. U., and Ozbayoglu, A. M · 2020
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What makes for good views for contrastive learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
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Traffic flow forecast through time series analysis based on deep learning
Zheng, J. and Huang, M · 2020
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Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W · 2021
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Non-stationary transformers: Exploring the stationarity in time series forecasting
Liu, Y., Wu, H., Wang, J., and Long, M · 2022
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Utilizing expert features for contrastive learning of time-series representations
Nonnenmacher, M. T., Oldenburg, L., Steinwart, I., and Reeb, D · 2022
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Learning latent seasonal-trend representations for time series forecasting
Wang, Z., Xu, X., Zhang, W., Trajcevski, G., Zhong, T., and Zhou, F · 2022
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CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting
Woo, G., Liu, C., Sahoo, D., Kumar, A., and Hoi, S · 2022
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Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Yang, L. and Hong, S · 2022
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Ts2vec: Towards universal representation of time series
Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., and Xu, B · 2022
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Are transformers effective for time series forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q · 2022
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