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

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Layer NormalizationPreprint

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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

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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

Battery health prediction under data scarcity: A cross-domain physics-informed 5-shot framework with GRU-Transformer

Nie, X., Pan, Y., Zhang, Y., Luo, Z., & Wang, S. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127012 · Cited by 25

Hierarchical forecasting with a top-down alignment of independent-level forecasts

Anderer, M., & Li, F. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.12.015 · Cited by 24

A novel integration framework for degradation-state prediction via transformer model with autonomous optimizing mechanism

Liu, Y., Chen, J., Chang, Y., He, S., & Zhou, Z. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.07.004 · Cited by 23

STTF: An Efficient Transformer Model for Traffic Congestion Prediction

Wang, X., Zeng, R., Zou, F., Liao, L., & Huang, F. (2023) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-022-00177-3 · Cited by 24

PHILNet: A novel efficient approach for time series forecasting using deep learning

Jiménez-Navarro, M. J., Martínez-Ballesteros, M., Alvarez, F. M., & Cortés, G. A. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.021 · Cited by 25

Research on the carbon emissions traceability inventory and multi-horizon prediction of ship carbon emissions: a case study of Tianjin Port

Li, Y., Xie, W., Yang, Y., Mei, Q., Wang, Z., Li, Z., & Wang, P. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1174411 · Cited by 28

Indoor temperature prediction with context-aware models in residential buildings

Kalidindi, S. S. V., Banaee, H., Karlsson, H., & Loutfi, A. (2023) · Building and Environment · doi:10.1016/j.buildenv.2023.110772 · Cited by 25

Evaluating different artificial neural network forecasting approaches for optimizing district heating network operation

Frison, L., Gölzhäuser, S., Bitterling, M., & Kramer, W. (2024) · Energy · doi:10.1016/j.energy.2024.132745 · Cited by 24

Time series land subsidence monitoring and prediction based on SBAS-InSAR and GeoTemporal transformer model

Zhang, J., Gao, J. F., & Gao, F. (2024) · Earth Science Informatics · doi:10.1007/s12145-024-01487-0 · Cited by 24

Motion-Inspired Spatial–Temporal Transformer for accurate vessel trajectory prediction

Qiang, H., Guo, Z., Chu, Z., Xie, S., & Peng, X. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110391 · Cited by 33

Enhanced N-BEATS for mid-term electricity demand forecasting

Kasprzyk, M., Pełka, P., Oreshkin, B. N., & Dudek, G. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113575 · Cited by 23

Combining meteorological and power information of station-measurement and model-prediction with the hybrid CNN-Transformer and CNN-BiLSTM for ultra-short-term photovoltaic power forecasting

Dai, Y., Wang, Y., Chen, Y., Wu, J., & Chao, J. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.111009 · Cited by 28

Artificial intelligence and classical statistical models for time series forecasting: a comprehensive review

Houssein, E. H., Mohamed, M., Younis, E. M. G., & Mohamed, W. M. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01318-z · Cited by 31

Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings

Ni, Z., Zhang, C., Karlsson, M., & Gong, S. (2023) · IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES) · doi:10.1109/ieses53571.2023.10253721 · Cited by 24

A multi-model deep learning approach to address prediction imbalances in smart greenhouses

Morales-García, J., Terroso-Sáenz, F., & Cecilia, J. M. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108537 · Cited by 23

Instability Pattern-Guided Model Updating Method for Data-Driven Transient Stability Assessment

Wang, H., Gao, F., Chen, Q., Bu, S., & Lei, C. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3429339 · Cited by 22

Dynamic modeling and defrost optimization for air source heat pumps: A deep learning and autoregression approach

Chen, L., Wang, W., Yang, X., Liu, H., & Ou, R. H. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114689 · Cited by 22

Weather Phenomena Monitoring: Optimizing Solar Irradiance Forecasting With Temporal Fusion Transformer

Hu, X. (2024) · IEEE Access · doi:10.1109/access.2024.3517144 · Cited by 21

Day-ahead photovoltaic power forecasting based on corrected numeric weather prediction and domain generalization

Liu, M., Lai, Z., Fang, Y., & Ling, Q. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115212 · Cited by 27

Mind the naive forecast! a rigorous evaluation of forecasting models for time series with low predictability

Beck, N., Dovern, J., & Vogl, S. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06268-w · Cited by 38

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Li, H., Heleno, M., Zhang, W., Sun, K., Garcia, L. R., & Hong, T. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115909 · Cited by 26

A systematic review of AI-driven business models for advancing Sustainable Development Goals

Khan, M. A., Rahman, A., Mahmud, F. U., Bishnu, K. K., Ahmed, M., Mridha, M. F., & Aung, Z. (2025) · Array · doi:10.1016/j.array.2025.100539 · Cited by 32

Data-Driven Battery Aging Mechanism Analysis and Degradation Pathway Prediction

Xu, R., Wang, Y., & Chen, Z. (2023) · Batteries · doi:10.3390/batteries9020129 · Cited by 22

Enhancing Inference on Physiological and Kinematic Periodic Signals via Phase-Based Interpretability and Multi-Task Learning

Soleimani, R., & Lobaton, E. J. (2022) · Information · doi:10.3390/info13070326 · Cited by 21

Global and Local Interattribute Relationships-Based Graph Convolutional Network for Flight Trajectory Prediction

Fan, Y., Tan, Y., Wu, L. Y., Ye, H., & Lyu, Z. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3357668 · Cited by 21

Analyzing spatio-temporal dynamics of dissolved oxygen for the River Thames using superstatistical methods and machine learning

He, H., Boehringer, T., Schäfer, B., Heppell, K., & Beck, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-72084-w · Cited by 23

Probabilistic simulation of electricity price scenarios using Conditional Generative Adversarial Networks

Walter, V., & Wagner, A. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100422 · Cited by 20

Applying machine learning to electricity price forecasting in simulated energy market scenarios

Nitsch, F., Schimeczek, C., & Bertsch, V. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.11.013 · Cited by 21

Groundwater level predictions in the Thames Basin, London over extended horizons using Transformers and advanced machine learning models

Ali, A. J., Ahmed, A. A., & Abbod, M. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.144300 · Cited by 22

Pedestrian trajectory prediction using goal-driven and dynamics-based deep learning framework

Wang, H., Zhi, W., Batista, G., & Chandra, R. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126557 · Cited by 22

Deep Learning for Time-Series Forecasting With Exogenous Variables in Energy Consumption: A Performance and Interpretability Analysis

Mohammadi, A., Nápoles, G., & Salgueiro, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3570618 · Cited by 25

ALGAN: Time Series Anomaly Detection with Adjusted-LSTM GAN

Bashar, M. A., & Nayak, R. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00810-2 · Cited by 33

Short-term electricity consumption forecasting with deep learning

Demir, E., & Günal, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07564-5 · Cited by 21

Deep learning in time series forecasting with transformer models and RNNs

dos Santos, R. P., Matos-Carvalho, J. P., & Leithardt, V. R. Q. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3001 · Cited by 28

Probabilistic forecasting for building energy systems using time-series foundation models

Park, Y., Germain, F. G., Liu, J., Wang, Y., Koike–Akino, T., Wichern, G., Azizan, N., Laughman, C. R., & Chakrabarty, A. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116446 · Cited by 27

Decomposition-Residuals Neural Networks: Hybrid System Identification Applied to Electricity Demand Forecasting

Theodorakos, K., Agudelo, O. M., Espinoza, M., & Moor, B. D. (2022) · IEEE Open Access Journal of Power and Energy · doi:10.1109/oajpe.2022.3145520 · Cited by 22

Enhancing stock movement prediction with market index and curriculum learning

Yang, J., Zhang, W., Zhang, X., Zhou, J., & Zhang, P. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118800 · Cited by 21

Economic system forecasting based on temporal fusion transformers: Multi-dimensional evaluation and cross-model comparative analysis

Han, Y., Tian, Y., Yu, L., & Gao, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126500 · Cited by 23

Short‐term traffic flow prediction based on AST‐MTL‐CNN‐GRU

Yang, Z., & Wang, C. (2023) · IET Intelligent Transport Systems · doi:10.1049/itr2.12400 · Cited by 23

Deep Learning Based Forecasting: A Case Study from the Online Fashion Industry

Kunz, M., Birr, S., Raslan, M., Ма, Л., & Januschowski, T. (2023) · Palgrave advances in the economics of innovation and technology · doi:10.1007/978-3-031-35879-1_11 · Cited by 32

Multi-Sensory Visual-Auditory Fusion of Wearable Navigation Assistance for People With Impaired Vision

Song, Y., Li, Z., Li, G., Wang, B., Zhu, M., & Shi, P. J. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3340335 · Cited by 21

Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer

Ye, H., Zhu, Q., & Zhang, X. (2024) · Energies · doi:10.3390/en17133061 · Cited by 21

Diffusion-based inpainting approach for multifunctional short-term load forecasting

Zhang, L., Jiang, Z., Ji, T., & Chen, Z. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124442 · Cited by 27

Forecasting CPI inflation under economic policy and geopolitical uncertainties

Sengupta, S., Chakraborty, T., & Singh, S. K. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.08.005 · Cited by 33

A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events

Burrichter, B., da Silva, J. K., Niemann, A., & Quirmbach, M. (2024) · Hydrology · doi:10.3390/hydrology11030041 · Cited by 21

On the Exploration of Temporal Fusion Transformers for Anomaly Detection with Multivariate Aviation Time-Series Data

Ayhan, B., Vargo, E. P., & Tang, H. (2024) · Aerospace · doi:10.3390/aerospace11080646 · Cited by 20

Intelligent fatigue damage tracking and prognostics of composite structures utilizing raw images via interpretable deep learning

Komninos, P., Verraest, A. E. C., Eleftheroglou, N., & Zarouchas, D. S. (2024) · Composites Part B Engineering · doi:10.1016/j.compositesb.2024.111863 · Cited by 21

A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring

Karkaria, V. N., Chen, J., Luey, C., Siuta, C., Lim, D., Radulescu, R., & Chen, W. (2024) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4067270 · Cited by 21

Enhancing deep learning for demand forecasting to address large data gaps

Riachy, C., He, M., Joneidy, S., Qin, S., Payne, T., Boulton, G., Occhipinti, A., & Angione, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126200 · Cited by 20

LSTM and Transformer-based framework for bias correction of ERA5 hourly wind speeds

Houndekindo, F., & Ouarda, T. B. M. J. (2025) · Energy · doi:10.1016/j.energy.2025.136498 · Cited by 25

A novel interpretable wind speed forecasting based on the multivariate variational mode decomposition and temporal fusion transformer

Xu, R., Fang, H., Zeng, H., & Wu, B. (2025) · Energy · doi:10.1016/j.energy.2025.136497 · Cited by 20

A Survey of Deep Learning for Time Series Forecasting: Theories, Datasets, and State-of-the-Art Techniques

Lu, G., Ou, Y. D., Wang, Z., Qu, Y., Xia, Y., Tang, D., Kotenko, I. V., & Li, W. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.068024 · Cited by 36

Artificial Intelligence in Endurance Sports: Metabolic, Recovery, and Nutritional Perspectives

Grivas, G. V., & Safari, K. (2025) · Nutrients · doi:10.3390/nu17203209 · Cited by 31

Earthquake Nowcasting with Deep Learning

Fox, G., Rundle, J. B., Donnellan, A., & Feng, B. (2022) · GeoHazards · doi:10.3390/geohazards3020011 · Cited by 22

A wind speed vector-wind power curve modeling method based on data denoising algorithm and the improved Transformer

Zha, W., Jin, Y., Sun, Y., & Li, Y. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108838 · Cited by 20

Federated learning for interpretable short-term residential load forecasting in edge computing network

Xu, C., Chen, G., & Li, C. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08130-3 · Cited by 19

Interpretable time series forecasting of NPP parameters in accident scenarios

Tohver, H., de Oliveira, R. M. S., & Jeltsov, M. (2023) · Nuclear Engineering and Design · doi:10.1016/j.nucengdes.2022.112145 · Cited by 20

Efficient Automated Deep Learning for Time Series Forecasting

Deng, D., Karl, F., Hutter, F., Bischl, B., & Lindauer, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26409-2_40 · Cited by 25

An Interval Prediction Approach of Wind Power Based on Skip-GRU and Block-Bootstrap Techniques

Quan, H., Zhang, W., Zhang, W., Li, Z., & Zhou, T. (2023) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2023.3270114 · Cited by 20

Predicting hate intensity of twitter conversation threads

Meng, Q., Suresh, T., Lee, R. K.-W., & Chakraborty, T. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110644 · Cited by 25

Touch-and-Heal

Guo, S., Zhan, L., Cao, Y., Chen, Z., Zhou, G., & Gong, J. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3596258 · Cited by 25

Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting

Sarkar, M. R., Anavatti, S. G., Dam, T., Pratama, M., & Kindhi, B. A. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191444 · Cited by 22

A Transformer-Based Prediction Method for Depth of Anesthesia During Target-Controlled Infusion of Propofol and Remifentanil

He, Y., Peng, S., Chen, M., Yang, Z., & Chen, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3305363 · Cited by 22

A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

Pesantez, J. E., Li, B., Lee, C., Zhao, Z., Butala, M. D., & Stillwell, A. S. (2023) · Energy · doi:10.1016/j.energy.2023.129142 · Cited by 20

Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data

Sim, S., Kim, D., & Bae, H. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3319557 · Cited by 26

Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network

Zhang, H., Wang, Z., & Vallery, H. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422672 · Cited by 20

Robust Instance-Based Semi-Supervised Learning Change Detection for Remote Sensing Images

Zuo, Y., Li, L., Liu, X., Gao, Z., Jiao, L., Liu, F., & Yang, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3379223 · Cited by 22

Unveiling the Multi-Dimensional Spatio-Temporal Fusion Transformer (MDSTFT): A Revolutionary Deep Learning Framework for Enhanced Multi-Variate Time Series Forecasting

Wang, S., Lin, Y., Jia, Y., Sun, J., & Yang, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3444788 · Cited by 21

Performance metrics for multi-step forecasting measuring win-loss, seasonal variance and forecast stability: an empirical study

Strøm, E., & Gundersen, O. E. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05715-4 · Cited by 27

A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations

Srivastava, M., & Tiwari, P. K. (2024) · Energy · doi:10.1016/j.energy.2024.133077 · Cited by 20

Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach

Lei, D., Qi, Y., Liu, S., Geng, D., Zhang, J., Hu, H., & Shen, Z. M. (2024) · Manufacturing & Service Operations Management · doi:10.1287/msom.2022.0453 · Cited by 19

Multi-Horizon Glucose Prediction Across Populations With Deep Domain Generalization

Zhu, T., Afentakis, I., Li, K., Armiger, R., Hill, N. E., Oliver, N. S., & Georgiou, P. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3428921 · Cited by 21

Deep Learning Framework for Accurate Static and Dynamic Prediction of CO2 Enhanced Oil Recovery and Storage Capacity

Xiao, Z., Shen, B., Yang, J., Yang, K., Zhang, Y., & Yang, S. (2024) · Processes · doi:10.3390/pr12081693 · Cited by 19

Hydroformer: Frequency Domain Enhanced Multi‐Attention Transformer for Monthly Lake Level Reconstruction With Low Data Input Requirements

Hou, M., Wei, J., Shi, Y., Hou, S., Zhang, W., Xu, J., Wu, Y., & Wang, H. (2024) · Water Resources Research · doi:10.1029/2024wr037166 · Cited by 19

Optimization of Wastewater Treatment Through Machine Learning-Enhanced Supervisory Control and Data Acquisition: A Case Study of Granular Sludge Process Stability and Predictive Control

Gulshin, I., & Kuzina, O. (2024) · Automation · doi:10.3390/automation6010002 · Cited by 19

Review of Non-Invasive Fetal Electrocardiography Monitoring Techniques

Li, X., Wan, J., & Peng, X. (2025) · Sensors · doi:10.3390/s25051412 · Cited by 19

Integrating Kolmogorov–Arnold Networks with Time Series Prediction Framework in Electricity Demand Forecasting

Zhang, Y., Cui, L., & Yan, W. (2025) · Energies · doi:10.3390/en18061365 · Cited by 23

Probabilistic Forecasts of Global Horizontal Irradiance for Solar Systems

Jønler, J. F., BrunoLottrup, F., Berg, B., Zhang, D., & Chen, K. (2022) · IEEE Sensors Letters · doi:10.1109/lsens.2022.3228783 · Cited by 19

A Temporal Fusion Transformer for Long-Term Explainable Prediction of Emergency Department Overcrowding

Caldas, F., & Soares, C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-23618-1_5 · Cited by 20

Evaluating reservoir performance using a transformer based proxy model

Zhang, F., Nghiem, L. D., & Chen, Z. (2023) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2023.211644 · Cited by 19

Predicting and understanding human action decisions during skillful joint-action using supervised machine learning and explainable-AI

Auletta, F., Kallen, R. W., di Bernardo, M., & Richardson, M. J. (2023) · Scientific Reports · doi:10.1038/s41598-023-31807-1 · Cited by 22

Forecasting Imbalance Price Densities With Statistical Methods and Neural Networks

Ganesh, V. N., & Bunn, D. W. (2023) · IEEE Transactions on Energy Markets Policy and Regulation · doi:10.1109/tempr.2023.3293693 · Cited by 22

Customized Uncertainty Quantification of Parking Duration Predictions for EV Smart Charging

Phipps, K., Schwenk, K., Briegel, B., Mikut, R., & Hagenmeyer, V. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299201 · Cited by 19

A Primer on Bayesian Neural Networks: Review and Debates

Arbel, J., Pitas, K., Vladimirova, M., & Fortuin, V. (2026) · Statistical Science · doi:10.1214/24-sts969 · Cited by 78

TrackFlow: Multi-Object Tracking with Normalizing Flows

Mancusi, G., Panariello, A., Porrello, A., Fabbri, M., Calderara, S., & Cucchiara, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00874 · Cited by 21

A robust electricity price forecasting framework based on heteroscedastic temporal Convolutional Network

Shi, W. X., & Wang, Y. F. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.110177 · Cited by 21

Application of three Transformer neural networks for short-term photovoltaic power prediction: A case study

Wu, J., Zhao, Y., Zhang, R., Li, X., & Wu, Y. (2024) · Solar Compass · doi:10.1016/j.solcom.2024.100089 · Cited by 19

Toward Resilient Electric Vehicle Charging Monitoring Systems: Curriculum Guided Multi-Feature Fusion Transformer

Li, D., Tang, J., Zhou, B., Cao, P., Hu, J. S., Leung, M.-F., & Wang, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3456843 · Cited by 19

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Zeng, H., Kou, Y., & Sun, X. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01223 · Cited by 19

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Buroni, G., Lebichot, B., & Bontempi, G. (2021) · IEEE Access · doi:10.1109/access.2021.3083412 · Cited by 21

HALO: HVAC Load Forecasting With Industrial IoT and Local–Global-Scale Transformer

Pan, C., Zhang, C., Ngai, E. C. ‐., Liu, J., & Li, B. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3401236 · Cited by 18

Interpretable wind speed forecasting through two-stage decomposition with comprehensive relative importance analysis

Zeng, H., Wu, B., Fang, H., & Lin, J. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126015 · Cited by 18

Temporal Fusion Transformer-Based Trading Strategy for Multi-Crypto Assets Using On-Chain and Technical Indicators

Lee, M.-C. (2025) · Systems · doi:10.3390/systems13060474 · Cited by 18

Multimodal Learning Techniques for Time Series Forecasting in Renewable Energy Systems: A Comprehensive Survey

Mansouri, M. M., Attouri, K., & Refaat, S. S. (2025) · IEEE Access · doi:10.1109/access.2025.3602914 · Cited by 19

Recurrent Neural Network-Augmented Locally Adaptive Interpretable Regression for Multivariate Time-Series Forecasting

Munkhdalai, L., Munkhdalai, T., Pham, V.-H., Li, M., Ryu, K. H., & Theera‐Umpon, N. (2022) · IEEE Access · doi:10.1109/access.2022.3145951 · Cited by 20

Portfolio Transformer for Attention-Based Asset Allocation

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