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Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results.
Forecasting natural gas consumption in istanbul using neural networks and multivariate time series methods
Demirel, Ö. F., Zaim, S., Çalişkan, A., and Özuyar, P · 2012
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Funnel: automatic mining of spatially coevolving epidemics
Matsubara, Y., Sakurai, Y., Van Panhuis, W. G., and Faloutsos, C · 2014
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Fast r-cnn
Girshick, R · 2015
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Causal discovery from temporally aggregated time series
Gong, M., Zhang, K., Schölkopf, B., Glymour, C., and Tao, D · 2017
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li, Y., Yu, R., Shahabi, C., and Liu, Y · 2017
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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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Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P. H., and Vedaldi, A · 2018
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Neural ordinary differential equations
Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
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Learning data-driven discretizations for partial differential equations
Bar-Sinai, Y., Hoyer, S., Hickey, J., and Brenner, M. P · 2019
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Learning to optimize multigrid pde solvers
Greenfeld, D., Galun, M., Basri, R., Yavneh, I., and Kimmel, R · 2019
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Learning neural pde solvers with convergence guarantees
Hsieh, J.-T., Zhao, S., Eismann, S., Mirabella, L., and Ermon, S · 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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Learning to infer implicit surfaces without 3d supervision
Liu, S., Saito, S., Chen, W., and Li, H · 2019
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Texture fields: Learning texture representations in function space
Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., and Geiger, A · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
Cited alongside, same era.
Latent ordinary differential equations for irregularly-sampled time series
Rubanova, Y., Chen, R. T., and Duvenaud, D. K · 2019
Cited alongside, same era.
Multivariate time series dataset for space weather data analytics
Angryk, R. A., Martens, P. C., Aydin, B., Kempton, D., Mahajan, S. S., Basodi, S., Ahmadzadeh, A., Cai, X., Filali Boubrahimi, S., Hamdi, S. M., et al · 2020
Cited alongside, same era.
Learning a neural 3d texture space from 2d exemplars
Henzler, P., Mitra, N. J., and Ritschel, T · 2020
Cited alongside, same era.
Fourier neural operator for parametric partial differential equations
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2020
Cited alongside, same era.
Time-series anomaly detection with implicit neural representation
Jeong, K.-J. and Shin, Y.-M · 2022
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Learning to accelerate partial differential equations via latent global evolution
Wu, T., Maruyama, T., and Leskovec, J · 2022
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Continuous pde dynamics forecasting with implicit neural representations
Yin, Y., Kirchmeyer, M., Franceschi, J.-Y., Rakotomamonjy, A., and Gallinari, P · 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R · 2022
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Nert: Implicit neural representations for general unsupervised turbulence mitigation
Jiang, W., Boominathan, V., and Veeraraghavan, A · 2023
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Dist: Rendering deep implicit signed distance function with differentiable sphere tracing
Liu, S., Zhang, Y., Peng, S., Shi, B., Pollefeys, M., and Cui, Z · 2020
Cited alongside, same era.
Implicit neural representations with periodic activation functions
Sitzmann, V., Martel, J., Bergman, A., Lindell, D., and Wetzstein, G · 2020
Cited alongside, same era.
Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., and Ng, R · 2020
Cited alongside, same era.
Message passing neural pde solvers
Brandstetter, J., Worrall, D. E., and Welling, M · 2021
Cited alongside, same era.
Neural operator: Learning maps between function spaces
Kovachki, N., Li, Z., Liu, B., Azizzadenesheli, K., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2021
Cited alongside, same era.
Learning nonlinear operators via deeponet based on the universal approximation theorem of operators
Lu, L., Jin, P., Pang, G., Zhang, Z., and Karniadakis, G. E · 2021
Cited alongside, same era.
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H., Xu, J., Wang, J., and Long, M · 2021
Cited alongside, same era.
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Revisiting long-term time series forecasting: An investigation on linear mapping
Li, Z., Qi, S., Li, Y., and Xu, Z · 2023
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Time series continuous modeling for imputation and forecasting with implicit neural representations
Naour, E. L., Serrano, L., Migus, L., Yin, Y., Agoua, G., Baskiotis, N., Guigue, V., et al · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2023
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Transformers in time series: A survey
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., and Sun, L · 2023
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Learning deep time-index models for time series forecasting
Woo, G., Liu, C., Sahoo, D., Kumar, A., and Hoi, S · 2023
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Timesnet: Temporal 2d-variation modeling for general time series analysis
Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M · 2023
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Are transformers effective for time series forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q · 2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Zhang, Y. and Yan, J · 2023
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iTransformer: Inverted transformers are effective for time series forecasting
Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., and Long, M · 2024
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