ABSTRACT
The multivariate fusion transformation is ubiquitous in multivariate time series prediction (MTSP) problems. The previous multivariate fusion transformation fuses the feature of different variates at a time step, then projects them to a new feature space for effective feature representation. However, temporal dependency is the most fundamental property of time series. The previous manner fails to capture the temporal dependency of the feature, which is destroyed in the transformed feature matrix. Multivariate feature extraction based on the feature matrix with missing temporal dependency leads to the loss of predictive performance of MTSP. To address this problem, we propose the Temporal Dependency Priority for Multivariate Time Series Prediction (TemDep) method. Specifically, TemDep extracts feature temporal dependency of multivariate time series first and then considers multivariate feature fusion. Moreover, the low-dimensional and high-dimensional feature fusion manners are designed with the temporal dependency priority to fit different dimensional multivariate time series. The extensive experimental results of different datasets show that our proposed method can outperform all state-of-the-art baseline methods. It proves the significance of temporal dependency priority for MTSP.
- Razvan-Gabriel Cirstea, Chenjuan Guo, Bin Yang, Tung Kieu, Xuanyi Dong, and Shirui Pan. 2022. Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version. arXiv preprint arXiv:2204.13767 (2022).Google Scholar
- Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. 2009. Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks. ACM. https://doi.org/10.1145/3209978.3210006Google ScholarDigital Library
- Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, and Schahram Dustdar. 2021. Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In Proceedings of the International Conference on Learning Representations. 1--11.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems , Vol. 30 (2017).Google Scholar
- Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems , Vol. 34 (2021), 22419--22430.Google Scholar
- Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. 2022. Are transformers effective for time series forecasting? arXiv preprint arXiv:2205.13504 (2022).Google Scholar
- Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. [n.,d.]. Informer: Beyond efficient transformer for long sequence time-series forecasting.Google Scholar
- Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2021. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence. 11106--11115.Google ScholarCross Ref
- Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In Proceedings of the International Conference on Machine Learning. 27268--27286.Google Scholar
Index Terms
- TemDep: Temporal Dependency Priority for Multivariate Time Series Prediction
Recommendations
Temporal self-attention-based Conv-LSTM network for multivariate time series prediction
AbstractTime series play an important role in many fields, such as industrial control, automated monitoring, and weather forecasting. Because there is often more than one variable in reality problems and they are related to each other, the ...
Multivariate Time Series Classification Based on MCNN-LSTMs Network
ICMLC '20: Proceedings of the 2020 12th International Conference on Machine Learning and ComputingMultivariate time series data has high latitude, variable length, coupling, multi-scale and other characteristics. The existing multivariate time series classification methods often extract a single type of feature through complex artificial feature ...
Using causal discovery for feature selection in multivariate numerical time series
Time series data contains temporal ordering, which makes its feature selection different from the normal feature selection. Feature selection in multivariate time series has two tasks: identifying the relevant features and finding their effective window ...
Comments