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Multivariate time series forecasting has been widely used in various practical scenarios.
Truncating the singular value decomposition for ill-posed problems
Rust, B. W · 1998
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Freeway performance measurement system: Mining loop detector data
Chen, C., Petty, K. F., Skabardonis, A., Varaiya, P. P., and Jia, Z · 2001
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Time series forecasting using a hybrid arima and neural network model
Zhang, G. P · 2003
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Stock price prediction using the arima model
Ariyo, A. A., Adewumi, A. O., and Ayo, C. K · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
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Vaswani, A., Shazeer, N. M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 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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Attend and diagnose: Clinical time series analysis using attention models
Song, H.-Z., Rajan, D., Thiagarajan, J. J., and Spanias, A · 2018
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Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Cdsa: Cross-dimensional self-attention for multivariate, geo-tagged time series imputation
Ma, J., Shou, Z., Zareian, A., Mansour, H., Vetro, A., and Chang, S.-F · 2019
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 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Fnet: Mixing tokens with fourier transforms
Lee-Thorp, J., Ainslie, J., Eckstein, I., and Ontañón, S · 2021
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Mlp-mixer: An all-mlp architecture for vision
Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Keysers, D., Uszkoreit, J., Lucic, M., and Dosovitskiy, A · 2021
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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
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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
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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. W · 2022
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Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Multivariate time series dataset for space weather data analytics
Angryk, R. A., Martens, P. C., Aydin, B., Kempton, D. J., Mahajan, S. S., Basodi, S., Ahmadzadeh, A., Cai, X., Boubrahimi, S. F., Hamdi, S. M., Schuh, M. A., and Georgoulis, M. K · 2020
Cited alongside, same era.
Towards efficient electricity forecasting in residential and commercial buildings: A novel hybrid cnn with a lstm-ae based framework
Khan, Z. A., Hussain, T., Ullah, A., Rho, S., Lee, M. Y., and Baik, S. W · 2020
Cited alongside, same era.
Synthesizer: Rethinking self-attention in transformer models
Tay, Y., Bahri, D., Metzler, D., Juan, D.-C., Zhao, Z., and Zheng, C · 2020
Cited alongside, same era.
Is attention better than matrix decomposition?
Geng, Z., Guo, M.-H., Chen, H., Li, X., Wei, K., and Lin, Z · 2021
Cited alongside, same era.
Reflash dropout in image super-resolution
Kong, X., Liu, X., Gu, J., Qiao, Y., and Dong, C · 2021
Cited alongside, same era.
SCINet: Time series modeling and forecasting with sample convolution and interaction
Liu, M., Zeng, A., Chen, M., Xu, Z., LAI, Q., Ma, L., and Xu, Q
Cited in the paper.
Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting
Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A. X., and Dustdar, S
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Metaformer: A unified meta framework for fine-grained recognition
Diao, Q., Jiang, Y., Wen, B., Sun, J., and Yuan, Z · 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., and Choo, J · 2022
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Etsformer: Exponential smoothing transformers for time-series forecasting
Woo, G., Liu, C., Sahoo, D., Kumar, A., and Hoi, S. C. H · 2022
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Are transformers effective for time series forecasting?
Zeng, A., Chen, M.-H., Zhang, L., and Xu, Q · 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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