Fetching the paper…
Reading the bibliography…
RNN-based methods have faced challenges in the Long-term Time Series Forecasting (LTSF) domain when dealing with excessively long look-back windows and forecast horizons.
Learning long-term dependencies with gradient descent is difficult
Bengio, Y.; Simard, P.; and Frasconi, P. 1994 · 1994
Earlier work this paper cites.
Long Short-Term Memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
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. 2021 · 2010
Earlier work this paper cites.
ImageNet Classification with Deep Convolutional Neural Networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network
An, N. H.; and Anh, D. T. 2015 · 2015
Earlier work this paper cites.
A Critical Review of Recurrent Neural Networks for Sequence Learning
Lipton, Z. C.; Berkowitz, J.; and Elkan, C. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L. u.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Bai, S.; Kolter, J. Z.; and Koltun, V. 2018 · 2018
Earlier work this paper cites.
Modeling long-and short-term temporal patterns with deep neural networks
Lai, G.; Chang, W.-C.; Yang, Y.; and Liu, H. 2018 · 2018
Earlier work this paper cites.
A Multi-Horizon Quantile Recurrent Forecaster
Wen, R.; Torkkola, K.; Narayanaswamy, B.; and Madeka, D. 2018 · 2018
Earlier work this paper cites.
Unsupervised Scalable Representation Learning for Multivariate Time Series
Franceschi, J.-Y.; Dieuleveut, A.; and Jaggi, M. 2019 · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Towards Better Forecasting by Fusing Near and Distant Future Visions
Cheng, J.; Huang, K.; and Zheng, Z. 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.
ViViT: A Video Vision Transformer
Arnab, A.; Dehghani, M.; Heigold, G.; Sun, C.; Lučić, M.; and Schmid, C. 2021 · 2021
Cited alongside, same era.
A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids
Aslam, S.; Herodotou, H.; Mohsin, S. M.; Javaid, N.; Ashraf, N.; and Aslam, S. 2021 · 2021
Cited alongside, same era.
A Review of Deep Learning Models for Time Series Prediction
Han, Z.; Zhao, J.; Leung, H.; Ma, K. F.; and Wang, W. 2021 · 2021
Cited alongside, same era.
Time-series forecasting with deep learning: a survey
Lim, B.; and Zohren, S. 2021 · 2021
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
Later among the works it cites.
NHITS: Neural Hierarchical Interpolation for Time Series Forecasting
Challu, C.; Olivares, K. G.; Oreshkin, B. N.; Garza Ramirez, F.; Mergenthaler Canseco, M.; and Dubrawski, A. 2023 · 2023
Closest in time.
Long-term Forecasting with TiDE: Time-series Dense Encoder
Das, A.; Kong, W.; Leach, A.; Mathur, S.; Sen, R.; and Yu, R. 2023 · 2023
Closest in time.
Han, L.; Ye, H.-J.; and Zhan, D.-C. 2023 · 2023
Closest in time.
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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. 2021 · 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 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
Bergsma, S.; Zeyl, T.; Rahimipour Anaraki, J.; and Guo, L. 2022 · 2022
Cited alongside, same era.
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Fan, W.; Zheng, S.; Yi, X.; Cao, W.; Fu, Y.; Bian, J.; and Liu, T.-Y. 2022 · 2022
Cited alongside, same era.
Masked Autoencoders Are Scalable Vision Learners
He, K.; Chen, X.; Xie, S.; Li, Y.; Dollár, P.; and Girshick, R. 2022 · 2022
Cited alongside, same era.
Li, Z.; Rao, Z.; Pan, L.; and Xu, Z. 2023 · 2023
Closest in time.
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
Nie, Y.; H. Nguyen, N.; Sinthong, P.; and Kalagnanam, J. 2023 · 2023
Closest in time.
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
Olivares, K. G.; Challu, C.; Marcjasz, G.; Weron, R.; and Dubrawski, A. 2023 · 2023
Closest in time.
Neural Differential Recurrent Neural Network with Adaptive Time Steps
Tan, Y.; Xie, L.; and Cheng, X. 2023 · 2023
Closest in time.
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting
Vijay, E.; Jati, A.; Nguyen, N.; Sinthong, G.; and Kalagnanam, J. 2023 · 2023
Closest in time.
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
Closest in time.
Transformers in time series: A survey
Wen, Q.; Zhou, T.; Zhang, C.; Chen, W.; Ma, Z.; Yan, J.; and Sun, L. 2023 · 2023
Closest in time.
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
Closest in time.
Are Transformers Effective for Time Series Forecasting?
Zeng, A.; Chen, M.; Zhang, L.; and Xu, Q. 2023 · 2023
Closest in time.
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting
Zhang, Y.; and Yan, J. 2023 · 2023
Closest in time.