[1]
V. L. Guen and N. Thome, Shape and time distortion loss for training deep time series forecasting models, arXiv preprint arXiv: 1909.09020, 2019.
[2]
Y. Zhu and D. Shasha, Statstream: Statistical monitoring of thousands of data streams in real time, in Proc. 28th Int. Conf. Very Large Data Bases, Hong Kong, China, 2002, pp. 358–369.
[7]
X. B. Jin, X. H. Yu, X. Y. Wang, Y. T. Bai, T. L. Su, and J. L. Kong, Prediction for time series with CNN and LSTM, in Proc. 11th Int. Conf. Modelling, Identification and Control (ICMIC2019), Tianjin, China, 2020, pp. 631–641.
[9]
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 Proc. 26th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Virtual Event, 2020, pp. 753–763.
[10]
H. Miao, Y. Zhang, Z. Ning, Z. Jiang, and L. Wang, TDG4MSF: A temporal decomposition enhanced graph neural network for multivariate time series forecasting, Appl. Intell.
[11]
B. N. Oreshkin, D. Carpo, N. Chapados, and Y. Bengio, N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, arXiv preprint arXiv: 1905.10437, 2019.
[16]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, arXiv preprint arXiv: 1706.03762, 2017.
[17]
S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y. X. Wang, and X. Yan, Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting, arXiv preprint arXiv: 1907.00235, 2019.
[19]
H. Wu, J. Xu, J. Wang, and M. Long, Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, arXiv preprint arXiv: 2106.13008, 2021.
[23]
X. Chen and L. Sun, Low-rank autoregressive tensor completion for multivariate time series forecasting, arXiv preprint arXiv: 2006.10436, 2020.
[25]
Z. Ning, Z. Jiang, H. Miao, and L. Wang, MST-GNN: A multi-scale temporal-enhanced graph neural network for anomaly detection in multivariate time series, in Web and Big Data, B. Li, L. Yue, C. Tao, X. Han, D. Calvanese, and T. Amagasa, eds. Cham, Switzerland: Springer, 2023, pp. 382–390.
[26]
A. Tran, A. Mathews, C. S. Ong, and L. Xie, Radflow: A recurrent, aggregated, and decomposable model for networks of time series, in Proc. Web Conf. 2021, Ljubljana, Slovenia, 2021, pp. 730–742.
[27]
S. Bai, J. Z. Kolter, and V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv: 1803.01271, 2018.
[29]
G. Lai, W. C. Chang, Y. Yang, and H. Liu, Modeling long- and short-term temporal patterns with deep neural networks, in Proc. SIGIR '18 : The 41st Int. ACM SIGIR Conf. Research & Development in Information Retrieval, Ann Arbor, MI, USA, 2018, pp. 95–104.
[30]
N. Kitaev, L. Kaiser, and A. Levskaya, Reformer: The efficient transformer, presented at the Int. Conf. Learning Representations (ICLR), New Orleans, LA, USA, 2019.
[32]
W. Xu, W. Liu, J. Bian, J. Yin, and T. Y. Liu, Instance-wise graph-based framework for multivariate time series forecasting, arXiv preprint arXiv: 2109.06489, 2021.