SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting

STL: A seasonal-trend decomposition

Robert B Cleveland, William S Cleveland, Jean E McRae, Irma Terpenning, et al · 1990

Earlier work this paper cites.

Adaptive mixtures of local experts

Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991 · 1991

Earlier work this paper cites.

Neural network studies. 1. Comparison of overfitting and overtraining

Igor V Tetko, David J Livingstone, and Alexander I Luik. 1995 · 1995

Earlier work this paper cites.

Recurrent neural networks

Larry R Medsker, Lakhmi Jain, et al · 2001

Earlier work this paper cites.

Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study

Ronald Boellaard, Nanda C Krak, Otto S Hoekstra, and Adriaan A Lammertsma. 2004 · 2004

Earlier work this paper cites.

Forecasting monthly and quarterly time series using STL decomposition

Marina Theodosiou. 2011 · 2011

Earlier work this paper cites.

Instance normalization: The missing ingredient for fast stylization

Original

Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016 · 2016

Earlier work this paper cites.

Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Original

Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017

Earlier work this paper cites.

Attention is all you need

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017

Earlier work this paper cites.

Deeptransport: Learning spatial-temporal dependency for traffic condition forecasting. In 2018 International Joint Conference on Neural Networks (IJCNN) . IEEE, 1–8

Xingyi Cheng, Ruiqing Zhang, Jie Zhou, and Wei Xu. 2018 · 2018

Earlier work this paper cites.

Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting

Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. 2019 · 2019

Earlier work this paper cites.

Pay less attention with lightweight and dynamic convolutions

Original

Felix Wu, Angela Fan, Alexei Baevski, Yann N Dauphin, and Michael Auli. 2019 · 2019

Earlier work this paper cites.

Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 5668–5675

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019 · 2019

Earlier work this paper cites.

Hippo: Recurrent memory with optimal polynomial projections

Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré. 2020 · 2020

Earlier work this paper cites.

Combining recurrent, convolutional, and continuous-time models with linear state space layers

Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré. 2021 · 2021

Earlier work this paper cites.

Recurrent neural networks for time series forecasting: Current status and future directions

Hansika Hewamalage, Christoph Bergmeir, and Kasun Bandara. 2021 · 2021

Earlier work this paper cites.

Reversible instance normalization for accurate time-series forecasting against distribution shift. In International conference on learning representations

Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, and Jaegul Choo. 2021 · 2021

Earlier work this paper cites.

Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In International conference on learning representations

Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, and Schahram Dustdar. 2021 · 2021

Earlier work this paper cites.

Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021 · 2021

Earlier work this paper cites.

Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 11106–11115

Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021 · 2021

Cited alongside, same era.

Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

William Fedus, Barret Zoph, and Noam Shazeer. 2022 · 2022

Cited alongside, same era.

Efficiently Modeling Long Sequences with Structured State Spaces. In International Conference on Learning Representations

Albert Gu, Karan Goel, and Christopher Re. 2022 · 2022

Cited alongside, same era.

Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning . PMLR, 27268–27286

Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022 · 2022

Cited alongside, same era.

Llm4ts: Two-stage fine-tuning for time-series forecasting with pre-trained llms

Large language models are zero-shot time series forecasters

Nate Gruver, Marc Finzi, Shikai Qiu, and Andrew G Wilson. 2024 · 2024

Closest in time.

Bi-Mamba4TS: Bidirectional Mamba for Time Series Forecasting

Original

Aobo Liang, Xingguo Jiang, Yan Sun, and Chang Lu. 2024 · 2024

Closest in time.

Jamba: A Hybrid Transformer-Mamba Language Model

Original

Opher Lieber, Barak Lenz, Hofit Bata, Gal Cohen, Jhonathan Osin, Itay Dalmedigos, Erez Safahi, Shaked Meirom, Yonatan Belinkov, Shai Shalev-Shwartz, Omri Abend, Raz Alon, Tomer Asida, Amir Bergman, Roman Glozman, Michael Gokhman, Avashalom Manevich, Nir Ratner, Noam Rozen, Erez Shwartz, Mor Zusman, and Yoav Shoham. 2024 · 2024

Closest in time.

Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models

Original

Chengkai Liu, Jianghao Lin, Jianling Wang, Hanzhou Liu, and James Caverlee. 2024b · 2024

Closest in time.

U-mamba: Enhancing long-range dependency for biomedical image segmentation