The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Multivariate time series forecasting via attention-based encoder–decoder framework
Abstract
Abstract Time series forecasting is an important technique to study the behavior of temporal data and forecast future values, which is widely applied in many fields, e.g. air quality forecasting, power load forecasting, medical monitoring, and intrusion detection. In this paper, we firstly propose a novel temporal attention encoder–decoder model to deal with the multivariate time series forecasting problem. It is an end-to-end deep learning structure that integrates the traditional encode context vector and temporal attention vector for jointly temporal representation learning, which is based on bi-directional long short-term memory networks (Bi-LSTM) layers with temporal attention mechanism as the encoder network to adaptively learning long-term dependency and hidden correlation features of multivariate temporal data. Extensive experimental results on five typical multivariate time series datasets showed that our model has the best forecasting performance compared with baseline methods.
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Cited by
Tripathy, K. P., & Mishra, A. K. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130458 · Cited by 336
Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data
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Multivariable time series forecasting using model fusion
Wang, R., Pei, X., Zhu, J., Zhang, Z., Huang, X., Zhai, J., & Zhang, F. (2022) · Information Sciences · doi:10.1016/j.ins.2021.11.025 · Cited by 39
Metadata
- Authors
- Du, S., Li, T., Yang, Y., & Horng, S.-J.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 388
- Pages
- 269-279
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3000499162
- Cited by
- 441
- References
- 41
- FWCI
- 34.60
- Type
- journal article
- Language
- en
- Open access
- closed
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