Fetching the paper…
Reading the bibliography…
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting have been proposed recently.
E. S. Gardner Jr, “Exponential smoothing: The state of the art,”
1985
Earlier work this paper cites.
H. Drucker, C. J. C. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, “Support vector regression machines,” in
1996
Earlier work this paper cites.
C. Chen, K. Petty, A. Skabardonis, P. Varaiya, and Z. Jia, “Freeway performance measurement system: mining loop detector data,”
2001
Earlier work this paper cites.
J. H. Friedman, “Greedy function approximation: a gradient boosting machine,”
2001
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.”
2008
Earlier work this paper cites.
K. Cho, B. van Merrienboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” in
2014
Earlier work this paper cites.
A. A. Ariyo, A. O. Adewumi, and C. K. Ayo, “Stock price prediction using the ARIMA model,” in
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in
2014
Earlier work this paper cites.
J. Garland, R. James, and E. Bradley, “Model-free quantification of time-series predictability,”
2014
Earlier work this paper cites.
X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in
2015
Earlier work this paper cites.
F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in
2017
Earlier work this paper cites.
Y.-C. Chen, “A tutorial on kernel density estimation and recent advances,”
2017
Earlier work this paper cites.
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “Lightgbm: A highly efficient gradient boosting decision tree,”
2017
Earlier work this paper cites.
G. Lai, W. Chang, Y. Yang, and H. Liu, “Modeling long- and short-term temporal patterns with deep neural networks,” in
2018
Earlier work this paper cites.
Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in
2018
Earlier work this paper cites.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in
2018
Earlier work this paper cites.
H. Yao, F. Wu, J. Ke, X. Tang, Y. Jia, S. Lu, P. Gong, J. Ye, and Z. Li, “Deep multi-view spatial-temporal network for taxi demand prediction,” in
2018
Earlier work this paper cites.
R. J. Hyndman and G. Athanasopoulos,
2018
Earlier work this paper cites.
Spyros Makridakis, “M4 dataset,” 2018. [Online]. Available:
2018
Earlier work this paper cites.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in
2019
Earlier work this paper cites.
S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y. Wang, and X. Yan, “Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,” in
2019
Earlier work this paper cites.
Z. Pan, Y. Liang, W. Wang, Y. Yu, Y. Zheng, and J. Zhang, “Urban traffic prediction from spatio-temporal data using deep meta learning,” in
2019
Earlier work this paper cites.
B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, “N-beats: Neural basis expansion analysis for interpretable time series forecasting,” in
2019
Earlier work this paper cites.
P. Xu, L. Yin, Z. Yue, and T. Zhou, “On predictability of time series,”
2019
Earlier work this paper cites.
S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in
2019
Earlier work this paper cites.
C. Zheng, X. Fan, C. Wang, and J. Qi, “GMAN: A graph multi-attention network for traffic prediction,” in
2020
Earlier work this paper cites.
C. Song, Y. Lin, S. Guo, and H. Wan, “Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,” in
2020
Cited alongside, same era.
D. Cao, Y. Wang, J. Duan, C. Zhang, X. Zhu, C. Huang, Y. Tong, B. Xu, J. Bai, J. Tong, and Q. Zhang, “Spectral temporal graph neural network for multivariate time-series forecasting,” in
2020
Cited alongside, same era.
L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” in
2020
Cited alongside, same era.
Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in
2020
Cited alongside, same era.
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra, “Beyond homophily in graph neural networks: Current limitations and effective designs,” in
J. Choi, H. Choi, J. Hwang, and N. Park, “Graph neural controlled differential equations for traffic forecasting,” in
2022
Later among the works it cites.
R. Cirstea, C. Guo, B. Yang, T. Kieu, X. Dong, and S. Pan, “Triformer: Triangular, variable-specific attentions for long sequence multivariate time series forecasting,” in
2022
Later among the works it cites.
C. Yang, H. Zhou, Z. An, X. Jiang, Y. Xu, and Q. Zhang, “Cross-image relational knowledge distillation for semantic segmentation,” in
2022
Later among the works it cites.
H. Wu, H. Zhou, M. Long, and J. Wang, “Interpretable weather forecasting for worldwide stations with a unified deep model,”
2023
Closest in time.
L. Sun, S. Gong, T. Zhang, F. Jiang, Z. Zhao, J. Chen, and X. Zhang, “SUFS: A generic storage usage forecasting service through adaptive ensemble learning,” in
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
D. Salinas, V. Flunkert, J. Gasthaus, and T. Januschowski, “Deepar: Probabilistic forecasting with autoregressive recurrent networks,”
2020
Cited alongside, same era.
S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The m4 competition: 100,000 time series and 61 forecasting methods,”
2020
Cited alongside, same era.
H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in
2021
Cited alongside, same era.
H. Wu, J. Xu, J. Wang, and M. Long, “Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,” in
2021
Cited alongside, same era.
Y. Chen, I. Segovia-Dominguez, and Y. R. Gel, “Z-gcnets: Time zigzags at graph convolutional networks for time series forecasting,” in
2021
Cited alongside, same era.
Z. Fang, Q. Long, G. Song, and K. Xie, “Spatial-temporal graph ODE networks for traffic flow forecasting,” in
2021
Cited alongside, same era.
J. Deng, X. Chen, R. Jiang, X. Song, and I. W. Tsang, “St-norm: Spatial and temporal normalization for multi-variate time series forecasting,” in
2021
Cited alongside, same era.
Closest in time.
Y. Zhang and J. Yan, “Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,” in
2023
Closest in time.
Y. Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, “A time series is worth 64 words: Long-term forecasting with transformers,” in
2023
Closest in time.
2023
Closest in time.
A. Zeng, M. Chen, L. Zhang, and Q. Xu, “Are transformers effective for time series forecasting?” in
2023
Closest in time.
C. Yu, F. Wang, Z. Shao, T. Sun, L. Wu, and Y. Xu, “Dsformer: A double sampling transformer for multivariate time series long-term prediction,” in
2023
Closest in time.
H. Wu, T. Hu, Y. Liu, H. Zhou, J. Wang, and M. Long, “Timesnet: Temporal 2d-variation modeling for general time series analysis,” in
2023
Closest in time.
2023
Closest in time.
Z. Chen, M. Ma, T. Li, H. Wang, and C. Li, “Long sequence time-series forecasting with deep learning: A survey,”
2023
Closest in time.
F. Wang, D. Yao, Y. Li, T. Sun, and Z. Zhang, “Ai-enhanced spatial-temporal data-mining technology: New chance for next-generation urban computing,”
2023
Closest in time.
F. Li, J. Feng, H. Yan, G. Jin, F. Yang, F. Sun, D. Jin, and Y. Li, “Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution,”
2023
Closest in time.
G. Jin, F. Li, J. Zhang, M. Wang, and J. Huang, “Automated dilated spatio-temporal synchronous graph modeling for traffic prediction,”
2023
Closest in time.
X. Wu, D. Zhang, M. Zhang, C. Guo, B. Yang, and C. S. Jensen, “Autocts+: Joint neural architecture and hyperparameter search for correlated time series forecasting,”
2023
Closest in time.
H. Liu, Z. Dong, R. Jiang, J. Deng, J. Deng, Q. Chen, and X. Song, “Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting,” in
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
C. Challu, K. G. Olivares, B. N. Oreshkin, F. G. Ramírez, M. M. Canseco, and A. Dubrawski, “NHITS: neural hierarchical interpolation for time series forecasting,” in
2023
Closest in time.
Y. Xu, F. Wang, Z. An, Q. Wang, and Z. Zhang, “Artificial intelligence for science—bridging data to wisdom,”
2023
Closest in time.
Y. Li, Z. Shao, Y. Xu, Q. Qiu, Z. Cao, and F. Wang, “Dynamic frequency domain graph convolutional network for traffic forecasting,” in
2024
Closest in time.
J. Deng, X. Chen, R. Jiang, D. Yin, Y. Yang, X. Song, and I. W. Tsang, “Disentangling structured components: Towards adaptive, interpretable and scalable time series forecasting,”
2024
Closest in time.
X. Liu, Y. Xia, Y. Liang, J. Hu, Y. Wang, L. Bai, C. Huang, Z. Liu, B. Hooi, and R. Zimmermann, “Largest: A benchmark dataset for large-scale traffic forecasting,”
2024
Closest in time.
T. Zhao, S. Wang, C. Ouyang, M. Chen, C. Liu, J. Zhang, L. Yu, F. Wang, Y. Xie, J. Li
2024
Closest in time.
C. Yu, F. Wang, Y. Wang, Z. Shao, T. Sun, D. Yao, and Y. Xu, “Mgsfformer: A multi-granularity spatiotemporal fusion transformer for air quality prediction,”
2024
Closest in time.
L. Huang, Y. Zeng, C. Yang, Z. An, B. Diao, and Y. Xu, “etag: Class-incremental learning via embedding distillation and task-oriented generation,” in
2024
Closest in time.