Temporal Fusion Transformers for interpretable multi-horizon time series forecasting

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

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A Unified Approach to Interpreting Model Predictions

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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Layer NormalizationPreprint

Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,762

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682

Kernel-based object tracking

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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

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Residual Attention Network for Image Classification

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073

An adaptive embedding procedure for time series forecasting with deep neural networks

Succetti, F., Rosato, A., & Panella, M. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.051 · Cited by 21

The role of artificial intelligence in crop improvement

Negus, K. L., Li, X., Welch, S. M., & Yu, J. (2024) · Advances in agronomy · doi:10.1016/bs.agron.2023.11.001 · Cited by 21

Probabilistic Temporal Fusion Transformers for Large-Scale KPI Anomaly Detection

Luo, H., Zheng, Y., Kang, C., & Zhao, S. (2024) · IEEE Access · doi:10.1109/access.2024.3353201 · Cited by 21

Hybrid Transformer Network for Soil Moisture Estimation in Precision Irrigation

Madhukumar, N., Wang, E., Everingham, Y. L., & Xiang, W. (2024) · IEEE Access · doi:10.1109/access.2024.3378257 · Cited by 18

Multi-Route Aircraft Trajectory Prediction Using Temporal Fusion Transformers

SILVESTRE, J., Mielgo, P., Bregón, A., Martínez‐Prieto, M. A., & Álvarez-Esteban, P. C. (2024) · IEEE Access · doi:10.1109/access.2024.3415419 · Cited by 24

Time history seismic response prediction of multiple homogeneous building structures using only one deep learning‐based Structure Temporal Fusion Network

Li, Z., Yang, Q., Deng, Q., Gong, Y., Tian, D., Su, P., & Teng, J. (2024) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.4213 · Cited by 18

Internet of things and ensemble learning-based mental and physical fatigue monitoring for smart construction sites

Kim, B., Preethaa, K. R. S., Song, S., Lukacs, R. R., An, J.-W., Chen, Z.-S., An, E., & Kim, S. (2024) · Journal of Big Data · doi:10.1186/s40537-024-00978-7 · Cited by 18

Windformer: A novel 4D high-resolution system for multi-step wind speed vector forecasting based on temporal shifted window multi-head self-attention

He, J., Hu, Z., Wang, S., Mujeeb, A., & Yang, P. (2024) · Energy · doi:10.1016/j.energy.2024.133206 · Cited by 18

Forecasting severe respiratory disease hospitalizations using machine learning algorithms

Albrecht, S., Broderick, D. T. J., Dost, K., Cheung, I. M. Y., Nghiem, N., Wu, M., Zhu, J., Poonawala-Lohani, N., Jamison, S., Rasanathan, D., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02702-0 · Cited by 18

MFformer: An improved transformer-based multi-frequency feature aggregation model for electricity load forecasting

Tong, H., & Liu, J. (2025) · Electric Power Systems Research · doi:10.1016/j.epsr.2025.111492 · Cited by 18

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series ModelsPreprint

Rabanser, S., Januschowski, T., Flunkert, V., Salinas, D., & Gasthaus, J. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.10111 · Cited by 26

Artificial intelligence-based power market price prediction in smart renewable energy systems: Combining prophet and transformer models

Huang, C., Zhao, T., Huang, D., Cen, B., Zhou, Q., & Chen, W. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38227 · Cited by 19

Simplicity in dynamic and competitive electricity markets: A case study on enhanced linear models versus complex deep-learning models for day-ahead electricity price forecasting

Mao, X., Chen, S., Yu, H., Duan, L., He, Y., & Chu, Y. (2025) · Applied Energy · doi:10.1016/j.apenergy.2024.125201 · Cited by 17

Adaptive filter-driven optimized attention-based CNN-LSTM for load forecasting in microgrids

Stefenon, S. F., & Yow, K. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107470 · Cited by 17

A Hybrid Federated Learning and Deep Learning Approach for Time Series Forecasting in Distributed System

K.Ramyalakshmi, Bansal, S. A., Midasala, V., Tanna, P., Mahender, U., & Bhagyalakshmi, L. (2025) · IEEE Madhya Pradesh Section Conference (MPCON) · doi:10.1109/mpcon66082.2025.11256749 · Cited by 17

Learning dynamic inter-farm dependencies for wind power forecasting via adaptive sparse graph attention network

Qu, K., Xue, S., Zheng, X., Yan, D., & Cao, H. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.124969 · Cited by 17

Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild

Yu, H., Vaessen, T., Myin‐Germeys, I., & Sano, A. (2021) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii52823.2021.9597459 · Cited by 17

Analysis and forecasting of rivers pH level using deep learning

Srivastava, A. K., & Cano, A. (2021) · Progress in Artificial Intelligence · doi:10.1007/s13748-021-00270-2 · Cited by 17

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection

Wang, C., Xing, S., Gao, R., Yan, L., Xiong, N. N., & Wang, R. (2023) · Sensors · doi:10.3390/s23031104 · Cited by 17

End-to-end learning with interpretation on electrohysterography data to predict preterm birth

Fischer, A. M., Rietveld, A. L., Teunissen, P. W., Bakker, P., & Hoogendoorn, M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106846 · Cited by 21

Exploring Transformer and Graph Convolutional Networks for Human Mobility Modeling

Corrias, R., Gjoreski, M., & Langheinrich, M. (2023) · Sensors · doi:10.3390/s23104803 · Cited by 21

Edge-Based Temporal Fusion Transformer for Multi-Horizon Blood Glucose Prediction

Zhu, T., Chen, T., Kuang, L., Zeng, J., Li, K., & Georgiou, P. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10181448 · Cited by 25

Multi-Task Learning and Temporal-Fusion-Transformer-Based Forecasting of Building Power Consumption

Ji, W., Cao, Z., & Li, X. (2023) · Electronics · doi:10.3390/electronics12224656 · Cited by 19

Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction

Huang, D., Liu, Z., & Wu, D. (2023) · Applied Sciences · doi:10.3390/app14010040 · Cited by 23

Modeling and prediction of key parameters of circulating fluidized bed boiler based on Transformer

Ye, Y., Lin, H., & Zhou, H. (2024) · Journal of the Taiwan Institute of Chemical Engineers · doi:10.1016/j.jtice.2024.105622 · Cited by 17

A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting

Rafi, M. A., Rodrigues, G. N., Mir, M. N. H., Bhuiyan, M. S. M., Mridha, M. F., Islam, M. R., & Watanobe, Y. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3538579 · Cited by 23

Correcting physics-based global tide and storm water level forecasts with the temporal fusion transformer

Cerrone, A. R., Westerink, L. G., Ling, G., Blakely, C., Wirasaet, D., Dawson, C., & Westerink, J. J. (2025) · Ocean Modelling · doi:10.1016/j.ocemod.2025.102509 · Cited by 17

Lithium-ion batteries remaining useful life prediction via Fourier-mixed window attention enhanced Informer with decomposition and adaptive error correction strategy

Cheng, F., Liu, H., & Lv, X. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103292 · Cited by 17

Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach

Khan, S., Mazhar, T., Shahzad, T., Ali, T. M., Ayaz, M., Ghadi, Y. Y., Aggoune, E. M., & Hamam, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-04301-z · Cited by 17

Managing Battery Performance Degradation Using Physics-Informed Learning Scheme for Multiple Health Indicators

Qin, L., Sun, T., Sun, X., & Xia, W. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3525742 · Cited by 16

Interpretable transformer model for national scale drought forecasting: Attention-driven insights across India

Pathania, A., & Gupta, V. (2025) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2025.106394 · Cited by 16

Online electric vehicle charging detection based on memory-based transformer using smart meter data

Kamoona, A. M., Song, H., Jalili, M., Wang, H., Razzaghi, R., & Yu, X. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126353 · Cited by 16

Towards a global spatial machine learning model for seasonal groundwater level predictions in Germany

Kunz, S., Schulz, A., Wetzel, M., Nölscher, M., Chiaburu, T., Bießmann, F., & Broda, S. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-3405-2025 · Cited by 16

Time series transformer for tourism demand forecasting

Yi, S., Chen, X., & Tang, C. (2025) · Scientific Reports · doi:10.1038/s41598-025-15286-0 · Cited by 26

Fine-grained ultra-short-term wind power forecasting based on Temporal Fusion Transformers integrated with turbine power time series clustering

Zhang, C., Xiao, X., Wang, Y., Hou, Z., Huang, S., Hu, W., Hu, M., & Huang, R. (2025) · Energy · doi:10.1016/j.energy.2025.137995 · Cited by 18

Explainable Artificial Intelligence (XAI): Concepts, Applications, Challenges, and Future Perspectives

Osman, K. A. Y. A., Shah, A. F. M. S., Karabulut, M. A., Karahan, S. N., Osmanca, M. S., & Acır, N. (2026) · IEEE Access · doi:10.1109/access.2026.3663161 · Cited by 24

ms-mamba: Multi-scale mamba for time-series forecasting

Karadag, Y. M., Talaz, I., Dino, İ. G., & Kalkan, S. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.133226 · Cited by 16

Interpretable Framework of Physics‐Guided Neural Network With Attention Mechanism: Simulating Paddy Field Water Temperature Variations

Xie, W., Kimura, M., Takaki, K., Asada, Y., Iida, T., & Jia, X. (2022) · Water Resources Research · doi:10.1029/2021wr030493 · Cited by 16

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics

Beattie, A., Mulink, P., Sahoo, S., Christou, I. T., Kalalas, C., Gutiérrez-Rojas, D., & Nardelli, P. H. J. (2022) · IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) · doi:10.1109/smartgridcomm52983.2022.9961002 · Cited by 18

Cyclic shear stress-strain prediction of saturated sand based on the unrolled seq2seq model and scheduled sampling

Meng, F., & Pei, H. (2022) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2022.107665 · Cited by 16

Deep learning algorithm development for river flow prediction: PNP algorithm

Bak, G.-M., & Bae, Y.-C. (2023) · Soft Computing · doi:10.1007/s00500-023-08254-1 · Cited by 17

Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers

Laborda, J., Ruano, S., & Zamanillo, I. (2023) · Mathematics · doi:10.3390/math11122625 · Cited by 21

Artificial Intelligent Power Forecasting for Wind Farm Based on Multi-Source Data Fusion

Wang, Q., Wang, Y., Zhang, K., Liu, Y., Qiang, W., & Wen, Q. H. (2023) · Processes · doi:10.3390/pr11051429 · Cited by 16

Lindorm TSDB: A Cloud-Native Time-Series Database for Large-Scale Monitoring Systems

Shen, C., Ouyang, Q., Li, F., Liu, Z., Zhu, L., Zou, Y., Su, Q., Yu, T., Yi, Y., Hu, J., et al. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611540.3611559 · Cited by 16

SolarFlux Predictor: A Novel Deep Learning Approach for Photovoltaic Power Forecasting in South Korea

Min, H., Hong, S., Song, J., Son, B., Noh, B., & Moon, J. (2024) · Electronics · doi:10.3390/electronics13112071 · Cited by 19

Unsupervised Anomaly Detection and Explanation in Network Traffic with Transformers

Kummerow, A., Abrha, E., Eisenbach, M., & Rösch, D. (2024) · Electronics · doi:10.3390/electronics13224570 · Cited by 22

Short-Term Load Forecasting Model Based on Time Series Clustering and Transformer in Smart Grid

Liu, Y., Zheng, R., Liu, M., Zhu, J., Zhao, X., & Zhang, M. (2025) · Electronics · doi:10.3390/electronics14020230 · Cited by 16

Generative probabilistic forecasting of wind power: A Denoising-Diffusion-based nonstationary signal modeling approach

Liu, J., Zang, H., Cheng, L., Ding, T., Wei, Z., & Sun, G. (2025) · Energy · doi:10.1016/j.energy.2025.134576 · Cited by 20

MQTransformer: Multi-Horizon Forecasts with Context Dependent and Feedback-Aware AttentionPreprint

Eisenach, C., Patel, Y., & Madeka, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.14799 · Cited by 42

Transformer-Based Water Quality Forecasting With Dual Patch and Trend Decomposition

Lin, Y., Qiao, J., Bi, J., Yuan, H., Wang, M., Zhang, J., & Zhou, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3514133 · Cited by 15

A comparative analysis of deep learning models for accurate spatio-temporal soil moisture prediction

Zhu, L., Dai, W., Huang, J., & Luo, Z. (2024) · Geocarto International · doi:10.1080/10106049.2024.2441382 · Cited by 17

Enhanced Interpretable Forecasting of Cryptocurrency Prices Using Autoencoder Features and a Hybrid CNN-LSTM Model

Badar, W., Ramzan, S., Raza, A., Fitriyani, N. L., Syafrudin, M., & Lee, S. W. (2025) · Mathematics · doi:10.3390/math13121908 · Cited by 15

Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models

Saleh, H., El–Sappagh, S., McCann, M., Alsamhi, S. H., & Breslin, J. G. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110406 · Cited by 16

Review on crack detection in civil infrastructure using structural health monitoring and machine learning techniques

Khan, M. Z., Shahzadi, M., Khan, A. A., Ali, U. A., Hassan, M. A. S., & Hussain, M. (2025) · Innovative Infrastructure Solutions · doi:10.1007/s41062-025-02147-y · Cited by 24

Predictive deep reinforcement learning with multi-agent systems for adaptive time series forecasting

Shaik, T., Tao, X., Li, L., Xie, H., Acharya, U. R., Gururajan, R., & Zhou, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113941 · Cited by 15

Temporal Fusion Transformer for forecasting vital sign trajectories in intensive care patients

Phetrittikun, R., Suvirat, K., Pattalung, T. N., Kongkamol, C., Ingviya, T., & Chaichulee, S. (2021) · Biomedical Engineering International Conference · doi:10.1109/bmeicon53485.2021.9745215 · Cited by 15

Sepformer-Based Models: More Efficient Models for Long Sequence Time-Series Forecasting

Fan, J., Wang, Z., Sun, D., & Wu, H. (2022) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2022.3230920 · Cited by 16

CBITS: Crypto BERT Incorporated Trading System

Kim, G., Kim, M.-S., Kim, B. C., & Lim, H. (2023) · IEEE Access · doi:10.1109/access.2023.3236032 · Cited by 16

EWareNet: Emotion-Aware Pedestrian Intent Prediction and Adaptive Spatial Profile Fusion for Social Robot Navigation

Narayanan, V., Manoghar, B. M., Rama Prashanth, R. V., & Bera, A. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161504 · Cited by 33

Intelligent Traction Inverter in Next Generation Electric Vehicles: The Health Monitoring of Silicon-Carbide Power Modules

Pino, C., Sitta, A., Castagnolo, G., Messina, A. A., Coffa, S., Calabretta, M., Scotti, F., Genovese, A., Piuri, V., Spampinato, C., et al. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3294726 · Cited by 16

Multi-Task Time Series Forecasting Based on Graph Neural Networks

Han, X., Huang, Y., Pan, Z., Li, W., Hu, Y., & Lin, G. (2023) · Entropy · doi:10.3390/e25081136 · Cited by 18

pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting

Zhou, Y., Chu, Z., Ruan, Y., Jin, G., Huang, Y., & Li, S. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/521 · Cited by 15

A novel loss function for neural network models exploring stock realized volatility using Wasserstein Distance

Souto, H. G., & Moradi, A. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100369 · Cited by 15

Interpretable EU ETS Phase 4 prices forecasting based on deep generative data augmentation approach

Liu, D., Chen, K., Cai, Y., & Tang, Z. (2024) · Finance research letters · doi:10.1016/j.frl.2024.105038 · Cited by 16

Modeling vehicle U-turning behavior near intersections: A deep learning approach based on TCN and multi-head attention

Zeng, W., Lin, Q., Zhu, B., Peng, C., & Yu, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123674 · Cited by 15

Enhancing deterministic prediction in unidirectional ocean waves using an Artificial Neural Network with exponential linear unit

Feng, Z.-Y., Wang, Z., Zheng, K., Li, R., Zhao, Y., & Wang, Y. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.117539 · Cited by 15

Network security AIOps for online stream data monitoring

Nguyen, G., Dlugolinský, Š., Tran, V., & García, Á. L. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09863-z · Cited by 18

Charting new avenues in financial forecasting with TimesNet: The impact of intraperiod and interperiod variations on realized volatility prediction

Souto, H. G. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124851 · Cited by 19

A dual-level graph attention network and transformer for enhanced trajectory prediction under road network constraints

Zhang, J., Guo, L., Wang, G. G., Yu, J. G., Zheng, X., Mei, Y., & Han, B. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125510 · Cited by 15

Aeroelastic force prediction via temporal fusion transformers

Montoya, M. C., Mishra, A., Verma, S., Mures, O. A., & Rubio-Medrano, C. E. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13381 · Cited by 16

Privacy-Preserving Graph Inference Network for Multi-Entity Wind Power Forecast: A Federated Learning Approach

Long, X., Ding, Y., Li, Y., Li, Y., Gao, L., & Zeng, Z. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3547227 · Cited by 15

MaGNet-BN: Markov-Guided Bayesian Neural Networks for Calibrated Long-Horizon Sequence Forecasting and Community Tracking

Qu, D., & Ma, Y. (2025) · Mathematics · doi:10.3390/math13172740 · Cited by 20

Vertical power flow forecast with LSTMs using regular training update strategies

Brauns, K., Scholz, C., Schultz, A., Baier, A., & Jost, D. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100143 · Cited by 16

Grid Load Forecasting Based on Dual Attention BiGRU and DILATE Loss Function

Wang, X., Wu, Z., Ge, J., Zhang, Z., Han, L., Wang, S., & Zhang, X. (2022) · IEEE Access · doi:10.1109/access.2022.3182334 · Cited by 17

U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting

Madhusudhanan, K., Burchert, J., Duong‐Trung, N., Born, S., & Schmidt-Thieme, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26422-1_3 · Cited by 15

IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning

Mu, B., Luo, X., Yuan, S., & Liang, X. (2023) · Geoscientific model development · doi:10.5194/gmd-16-4677-2023 · Cited by 15

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series ForecastingPreprint

Park, Y., Maddix, D. C., Aubet, F.-X., Kan, K., Gasthaus, J. A., & Wang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06581 · Cited by 15

Researching the landscape of predictive emissions monitoring system: a review of literature and technology trends

Wang, F., Pang, Y., Bai, L., & Godin, M. (2025) · ENVIRONMENTAL SYSTEMS RESEARCH · doi:10.1186/s40068-025-00403-9 · Cited by 14

Retail Demand Forecasting: A Comparative Analysis of Deep Neural Networks and the Proposal of LSTMixer, a Linear Model Extension

Theodoridis, G., & Tsadiras, A. K. (2025) · Information · doi:10.3390/info16070596 · Cited by 14

Multistep PV power forecasting using deep learning models and the reptile search algorithm

Al‐Dahidi, S., Alahmer, H., Rinchi, B., Bani-Abdullah, A., Alrbai, M., Ayadi, O., & Al‐Ghussain, L. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106265 · Cited by 14

Short-term smart grid energy forecasting using a hybrid deep learning method on univariate and multivariate data sets

Gomez, W., Wang, F., & Sheu, S. (2025) · Energy · doi:10.1016/j.energy.2025.138081 · Cited by 15

Failure analysis and corrosion prediction of scratched coating/steel system in marine atmosphere using semantic segmentation and temporal prediction

Bao, Y., Xie, G., Pu, P., Yao, X., Jin, W., & 夏大海 (2025) · Engineering Failure Analysis · doi:10.1016/j.engfailanal.2025.109912 · Cited by 14

A review on multi-sensor data fusion for wearable health monitoring

John, A., James, A. P., Cardiff, B., & John, D. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104319 · Cited by 33

Intelligent prediction method of rockburst driven by microseismic temporal features in deep near-vertical coal seams

Xu, H., Li, K., Lai, X., Shan, P., Zhang, B., Dai, L., Yang, S., Guo, Q., Xi, X., & Yan, Z. (2026) · International Journal of Minerals Metallurgy and Materials · doi:10.1007/s12613-026-3408-7 · Cited by 14

DNN-based multi-output model for predicting soccer team tactics

Lee, G. J., & Jung, J. J. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.853 · Cited by 14

Learning Generative RNN-ODE for Collaborative Time-Series and Event Sequence Forecasting

Li, L., Yan, J., Zhang, Y., Zhang, J., Bao, J., Jin, Y., & Yang, X. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3185115 · Cited by 14

An Improved Temporal Fusion Transformers Model for Predicting Supply Air Temperature in High-Speed Railway Carriages

Feng, G., Zhang, L., Ai, F., Zhang, Y., & Hou, Y. (2022) · Entropy · doi:10.3390/e24081111 · Cited by 14

Short-Term Online Forecasting for Passenger Origin–Destination (OD) Flows of Urban Rail Transit: A Graph–Temporal Fused Deep Learning Method

Zheng, H., Chen, J., Huang, Z., Yang, K., & Zhu, J. (2022) · Mathematics · doi:10.3390/math10193664 · Cited by 14

Predicting network flows from speeds using open data and transfer learning

Mahajan, V., Cantelmo, G., Rothfeld, R., & Antoniou, C. (2022) · IET Intelligent Transport Systems · doi:10.1049/itr2.12305 · Cited by 14

A novel interpretable model ensemble multivariate fast iterative filtering and temporal fusion transform for carbon price forecasting

Wang, Y., Zhong, W., Kang, X., & Luo, Y. (2022) · Energy Science & Engineering · doi:10.1002/ese3.1380 · Cited by 14

A hybrid neural network based model for blood donation forecasting

Ding, X., Zhang, X., Li, X., & Du, J. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104488 · Cited by 14

Forecasting of PV Plant Output Using Interpretable Temporal Fusion Transformer Model

Islam, M., Shuvo, S. S., Shohan, M. J. A., & Faruque, O. M. (2023) · North American Power Symposium (NAPS) · doi:10.1109/naps58826.2023.10318698 · Cited by 14

Integration of harvester trajectory and satellite imagery for large-scale winter wheat mapping using deep positive and unlabeled learning

Xiong, X., Yang, J., Zhong, R., Dong, J., Huang, J., Ting, K. C., Ying, Y. B., & Lin, T. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108487 · Cited by 14

Blood glucose forecasting from temporal and static information in children with T1D

Marx, A., Stefano, F. D., Leutheuser, H., Chin-Cheong, K., Pfister, M., Burckhardt, M., Bachmann, S., & Vogt, J. E. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.1296904 · Cited by 15

A Deep Learning Model with a Self-Attention Mechanism for Leg Joint Angle Estimation across Varied Locomotion Modes

Ding, G., Georgilas, I. P., & Plummer, A. R. (2023) · Sensors · doi:10.3390/s24010211 · Cited by 14

FuMeAuth: Sensor-Based Continuous Authentication Using Fused Memory-Augmented Transformer Autoencoder

Li, Y., Huang, Y., & Huang, H. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3394437 · Cited by 14

TRECK: Long-Term Traffic Forecasting With Contrastive Representation Learning

Zheng, X., Bagloee, S. A., & Sarvi, M. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3421328 · Cited by 16

Improved NaS Battery State of Charge and State of Health Estimation: A Novel Integration of Temporal Fusion Transformer, Isolation Forest, and Support Vector Regression

Almarzooqi, A. H., Alhusin, M. O., Nikolakakos, I. P., Salih, M., Husnain, A., & Albeshr, H. (2024) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2024.3451408 · Cited by 14

Multi-Way adaptive Time Aware LSTM for irregularly collected sequential ICU data

Cissoko, M. B. H., Castelain, V., & Lachiche, N. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125548 · Cited by 14

CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland

Kraft, B., Schirmer, M., Aeberhard, W. H., Zappa, M., Seneviratne, S. I., & Gudmundsson, L. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-1061-2025 · Cited by 20

Temporal Fusion Transformers With Hierarchical Attention for Long-Horizon Multivariate Time-Series Forecasting

Park, H. J., Santos, G. M., & Kozlov, V. T. (2023) · Princeton Journal of Pre-Collegiate Research · doi:10.67698/pjpcr.2026.0121

Multi-horizon Time Series Forecasting Using Temporal Fusion Transformer

Kim, I., Kim, D., & Lee, J. (2021) · Proceedings of the Korea Information Processing Society Conference · doi:10.3745/pkips.y2021m05a.479

Interpretable Multivariate Time Series Forecasting with Temporal Attention Convolutional Neural Networks

Pantiskas, L., Verstoep, K., & Bal, H. E. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308570 · Cited by 30

Temporal Saliency Detection Towards Explainable Transformer-Based Timeseries Forecasting

Duong‐Trung, N., Nguyen-Duc, M., & Le-Phuoc, D. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50396-2_14 · Cited by 8

TFformer: A time–frequency domain bidirectional sequence-level attention based transformer for interpretable long-term sequence forecasting

Zhao, T., Fang, L., Ma, X., Li, X., & Zhang, C. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110994 · Cited by 44