Parallel spatio-temporal attention-based TCN for multivariate time series prediction

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Exploiting Generative AI to Scale up Intelligent Tutoring Systems

Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891

Untitled work

(2016) · Cited by 11,679

Learning long-term dependencies with gradient descent is difficult

Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Bai, S., Kolter, J. Z., & Koltun, V. (2018) · arXiv · doi:10.48550/arxiv.1803.01271 · Cited by 4,655

Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

Box, G. E. P., & Pierce, D. A. (1970) · Journal of the American Statistical Association · doi:10.1080/01621459.1970.10481180 · Cited by 3,234

Deep learning and its applications to machine health monitoring

Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., & Cottrell, G. W. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/366 · Cited by 1,585

Language Modeling with Gated Convolutional Networks

Dauphin, Y., Fan, A., Auli, M., & Grangier, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.08083 · Cited by 1,080

Regularizing and Optimizing LSTM Language Models

Merity, S., Keskar, N. S., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1708.02182 · Cited by 697

Deep Learning with Long Short-Term Memory for Time Series Prediction

Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z., & Zhang, H. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.1800155 · Cited by 629

Data driven prediction models of energy use of appliances in a low-energy house

Candanedo, L. M., Feldheim, V., & Deramaix, D. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.01.083 · Cited by 554

GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

Liang, Y., Ke, S., Zhang, J., Yi, X., & Zheng, Y. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/476 · Cited by 510

Financial time series prediction using least squares support vector machines within the evidence framework

Van Gestel, T., Suykens, J. A. K., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., De Moor, B., & Vandewalle, J. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.935093 · Cited by 499

EA-LSTM: Evolutionary attention-based LSTM for time series prediction

Li, Y., Zhu, Z., Kong, D., Han, H., & Zhao, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.05.028 · Cited by 489

A Survey on Activity Detection and Classification Using Wearable Sensors

Cornacchia, M., Ozcan, K., Zheng, Y., & Velipasalar, S. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2016.2628346 · Cited by 424

Neural Machine Translation in Linear Time

Kalchbrenner, N., Espeholt, L., Simonyan, K., Oord, A. V. D., Graves, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1610.10099 · Cited by 414

A Convolutional Encoder Model for Neural Machine Translation

Gehring, J., Auli, M., Grangier, D., & Dauphin, Y. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1012 · Cited by 405

DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction

Liu, Y., Gong, C., Yang, L., & Chen, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113082 · Cited by 387

Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data

Zhu, J., Ge, Z., Song, Z., & Gao, F. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.09.003 · Cited by 308

A dual-stage two-phase model of selective attention.

Hübner, R., Steinhauser, M., & Lehle, C. (2010) · Psychological Review · doi:10.1037/a0019471 · Cited by 267

DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting

Huang, S., Wang, D., Wu, X., & Tang, A. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3358132 · Cited by 250

Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis

Hou, L., & Bergmann, N. W. (2012) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2200817 · Cited by 249

Distributed and parallel time series feature extraction for industrial big data applications

Christ, M., Kempa-Liehr, A. W., & Feindt, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.07717 · Cited by 221

Deep Learning for Community Detection: Progress, Challenges and Opportunities

Liu, F., Xue, S., Wu, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Yang, J., & Yu, P. S. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/693 · Cited by 198

Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications

Xu, Y., Sun, Y., Wan, J., Liu, X., & Song, Z. (2017) · IEEE Access · doi:10.1109/access.2017.2731945 · Cited by 173

Bag Constrained Structure Pattern Mining for Multi-Graph Classification

Wu, J., Zhu, X., Zhang, C., & Yu, P. S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.2297923 · Cited by 168

On-line learning of indoor temperature forecasting models towards energy efficiency

Zamora-Martínez, F., Romeu, P., Botella-Rocamora, P., et al. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.04.034 · Cited by 141

Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning

Yan, H., Wan, J., Zhang, C., Tang, S., Hua, Q., & Wang, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2809681 · Cited by 130

Laplacian Echo State Network for Multivariate Time Series Prediction

Han, M., & Xu, M. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2574963 · Cited by 120

Stock Price Prediction Using Attention-based Multi-Input LSTM

Li, H., Shen, Y., & Zhu, Y. (2018) · Asian Conference on Machine Learning · Cited by 115

Boosting for Multi-Graph Classification

Wu, J. X., Pan, S., Xingquan Zhu, A., & Cai, Z. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2327111 · Cited by 108

Ensemble of evolving data clouds and fuzzy models for weather time series prediction

Soares, E. A., Costa, P., Costa, B. S. J., & Leite, D. F. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.12.032 · Cited by 94

Multi-Instance Learning with Discriminative Bag Mapping

Wu, J. X., Pan, S., Xingquan Zhu, A., Zhang, C., & Wu, X. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2788430 · Cited by 84

Temporal Convolutional Attention-based Network For Sequence ModelingPreprint

Hao, H., Wang, Y., Xue, S., Xia, Y., Zhao, J., & Shen, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12530 · Cited by 69

Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection

Dai, R., Minciullo, L., Garattoni, L., Francesca, G., & Brémond, F. (2019) · Advanced Video and Signal Based Surveillance · doi:10.1109/avss.2019.8909841 · Cited by 29

Industrial process time-series modeling based on adapted receptive field temporal convolution networks concerning multi-region operations

Wang, Y., & Li, H. (2020) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2020.106877 · Cited by 20

AEDmts: An Attention-Based Encoder-Decoder Framework for Multi-Sensory Time Series Analytic

Fan, J., Wang, H., Huang, Y., Zhang, K., & Zhao, B. (2020) · IEEE Access · doi:10.1109/access.2020.2971579 · Cited by 6

GA-mADAM-IIoT: A new lightweight threats detection in the industrial IoT via genetic algorithm with attention mechanism and LSTM on multivariate time series sensor data

Saheed, Y. K., Omole, A. I., & Sabit, M. O. (2025) · Sensors International · doi:10.1016/j.sintl.2024.100297 · Cited by 88