The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Multi-Horizon Time Series Forecasting with Temporal Attention Learning
Abstract
We propose a novel data-driven approach for solving multi-horizon probabilistic forecasting tasks that predicts the full distribution of a time series on future horizons. We illustrate that temporal patterns hidden in historical information play an important role in accurate forecasting of long time series. Traditional methods rely on setting up temporal dependencies manually to explore related patterns in historical data, which is unrealistic in forecasting long-term series on real-world data. Instead, we propose to explicitly learn constructing hidden patterns' representations with deep neural networks and attending to different parts of the history for forecasting the future.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Deep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Attention-Based Multimodal Fusion for Video Description
Hori, C., Hori, T., Lee, T.-Y., Zhang, Z., Harsham, B., Hershey, J. R., Marks, T. K., & Sumi, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.450 · Cited by 401
Cited by
Temporal Fusion Transformers for interpretable multi-horizon time series forecasting
Lim, B., Arık, S. Ö., Loeff, N., & Pfister, T. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.03.012 · Cited by 3,291
Dynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
NHITS: Neural Hierarchical Interpolation for Time Series Forecasting
Challu, C., Olivares, K. G., Oreshkin, B. N., Garza Ramirez, F., Mergenthaler Canseco, M., & Dubrawski, A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i6.25854 · Cited by 537
Jacobson, N. C., & Bhattacharya, S. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104013 · Cited by 92
Small perturbations are enough: Adversarial attacks on time series prediction
Wu, T., Wang, X., Qiao, S., Xian, X., Liu, Y., & Zhang, L. (2022) · Information Sciences · doi:10.1016/j.ins.2021.11.007 · Cited by 72
Metadata
- Authors
- Fan, C., Zhang, Y., Pan, Y., Li, X., Zhang, C., Yuan, R., Wu, D., Wang, W., Pei, J., & Huang, H.
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 2527-2535
- OpenAlex
- W2949468773
- Cited by
- 205
- References
- 6
- FWCI
- 15.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fan, C., Zhang, Y., Pan, Y., Li, X., Zhang, C., Yuan, R., Wu, D., Wang, W., Pei, J., & Huang, H. (2019). Multi-Horizon Time Series Forecasting with Temporal Attention Learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330662
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