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The performance of transformers for time-series forecasting has improved significantly.
Individual comparisons by ranking methods
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ARMA models and the Box–Jenkins methodology
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Forecasting seasonals and trends by exponentially weighted moving averages
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Batch normalization: Accelerating deep network training by ueducing internal covariate shift
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Adam: A method for stochastic optimization
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U-Net: Convolutional networks for biomedical image segmentation
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Trindade, A. (2015) · 2015
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Multi-scale convolutional neural networks for time series classification
Cui, Z., Chen, W., and Chen, Y. (2016) · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016) · 2016
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Multi-scale context aggregation by dilated convolutions
Yu, F. and Koltun, V. (2016) · 2016
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Feature pyramid networks for object detection
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Aidan N Gomez, Kaiser, L., and Polosukhin, I. (2017) · 2017
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A multi-horizon quantile recurrent forecaster
Wen, R., Torkkola, K., Narayanaswamy, B., and Madeka, D. (2017) · 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) · 2018
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DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. (2018) · 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) · 2018
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Self-attention with relative position representations
Shaw, P., Uszkoreit, J., and Vaswani, A. (2018) · 2018
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Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., and Salakhutdinov, R. (2019) · 2019
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Music transformer: Generating music with long-term structure
Huang, C.-Z. A., Vaswani, A., Uszkoreit, J., Simon, I., Hawthorne, C., Shazeer, N., Dai, A. M., Hoffman, M. D., Dinculescu, M., and Eck, D. (2019) · 2019
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Wu, H., Xu, J., Wang, J., and Long, M. (2021) · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021) · 2021
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N-HiTS: Neural hierarchical interpolation for time series forecasting
Challu, C., Olivares, K. G., Oreshkin, B. N., Garza, F., Mergenthaler-Canseco, M., and Dubrawski, A. (2022) · 2022
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Reversible instance normalization for accurate time-series forecasting against distribution shift
Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.-H., and Choo, J. (2022) · 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) · 2022
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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) · 2019
Cited alongside, same era.
Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Sen, R., Yu, H.-F., and Dhillon, I. S. (2019) · 2019
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2020) · 2020
Cited alongside, same era.
N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y. (2020) · 2020
Cited alongside, same era.
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J., and Januschowski, T. (2020) · 2020
Cited alongside, same era.
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Smyl, S. (2020) · 2020
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Time-aware multi-scale RNNs for time series modeling
Chen, Z., Ma, Q., and Lin, Z. (2021) · 2021
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Token merging: Your ViT but faster
Bolya, D., Fu, C.-Y., Dai, X., Zhang, P., Feichtenhofer, C., and Hoffman, J. (2023) · 2023
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Token pooling in vision transformers for image classification
Marin, D., Chang, J.-H. R., Ranjan, A., Prabhu, A., Rastegari, M., and Tuzel, O. (2023) · 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) · 2023
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Scaleformer: Iterative multi-scale refining transformers for time series forecasting
Shabani, M. A., Abdi, A. H., Meng, L., and Sylvain, T. (2023) · 2023
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TSMixer: Lightweight MLP-mixer model for multivariate time series forecasting
Vijay, E., Jati, A., Nguyen, N., Sinthong, G., and Kalagnanam, J. (2023) · 2023
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MICN: Multi-scale local and global context modeling for long-term series forecasting
Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., and Xiao, Y. (2023) · 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) · 2023
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Are transformers effective for time series forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q. (2023) · 2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Zhang, Y. and Yan, J. (2023) · 2023
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