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

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

Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560

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

SPPformer: A transformer-based model with a sparse attention mechanism for comprehensive and interpretable ship price analysis

Wang, W., Luo, Y., Xu, Y., Liu, D., Zhou, J., & Shao, P. (2025) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2025.104136 · Cited by 14

Show Me What You’re Looking For

Schwenke, L., & Atzmueller, M. (2021) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.v34i1.128399 · Cited by 15

How Large AI Model Empowers Time-Series Forecasting for the Operation and Maintenance of Industrial Automation System?

Zhang, L., Cheng, W., Zhang, S., Xing, J., Nie, Z., Chen, X., Lan, D., Liu, Y., Yang, Y., & Pang, Z. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3575118 · Cited by 17

Multi-source heterogeneous data fusion and intelligent prediction modeling for chemical engineering construction projects based on improved transformer architecture

Xin, D. (2025) · Scientific Reports · doi:10.1038/s41598-025-22752-2 · Cited by 16

Transfer learning techniques on temporal fusion transformers for short-term building load forecasting under limited data conditions

Zakynthinos, A., Michalakopoulos, V., Sarmas, E., & Marinakis, V. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116935 · Cited by 13

A Comprehensive Review for Agricultural Product Prices Forecasting: Architectural Diversity and Open Challenges

Wu, B., Wang, J., Li, Q., Fu, D., & Wang, L. (2026) · Expert Systems · doi:10.1111/exsy.70254 · Cited by 13

Seismic reliability analysis of building structures using subset simulation coupled with deep learning-based surrogate model

Nguyen, T.-T., Ha, M.-H., Nguyen, T.-P., & Dang, V.-H. (2022) · Advances in Structural Engineering · doi:10.1177/13694332221092677 · Cited by 13

Data-driven Kernel Subspace Clustering with Local Manifold Preservation

Xu, K., Chen, L., & Wang, S. (2022) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw58026.2022.00116 · Cited by 13

Model Selection for Time Series Forecasting An Empirical Analysis of Multiple Estimators

Cerqueira, V., Torgo, L., & Soares, C. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11239-8 · Cited by 13

Attention based convolutional networks for traffic flow prediction

Lin, J., Lin, C., & Ye, Q. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15395-w · Cited by 13

ENCODE - Ensemble neural combination for optimal dimensionality encoding in time-series forecasting

Giampaolo, F., Gatta, F., Prezioso, E., Cuomo, S., Zhou, M., Fortino, G., & Piccialli, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101918 · Cited by 13

Automatic generation of fuzzy inference systems for multivariate time series forecasting

Carvalho, T. M., Vellasco, M. M. B. R., & Amaral, J. F. (2023) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2023.108657 · Cited by 17

Multi-Band Measurements for Deep Learning-Based Dynamic Channel Prediction and Simulation

Pasic, F., Cicco, N. D., Skocaj, M., Tornatore, M., Schwarz, Š., Mecklenbräuker, C. F., & Oegli-Esposti, V. (2023) · IEEE Communications Magazine · doi:10.1109/mcom.003.2200718 · Cited by 13

Short-term forecasting of ozone air pollution across Europe with transformers

Hickman, S. H. M., Griffiths, P. T., Nowack, P. J., & Archibald, A. T. (2023) · Environmental Data Science · doi:10.1017/eds.2023.37 · Cited by 15

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model

Pillay, M. T., Minakawa, N., Kim, Y., Kgalane, N., Ratnam, J. V., Behera, S. K., Hashizume, M., & Sweijd, N. (2023) · Scientific Reports · doi:10.1038/s41598-023-50176-3 · Cited by 13

Harnessing the power of transformers and data fusion in smart irrigation

Deforce, B., Baesens, B., Diels, J., & Serral, E. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111246 · Cited by 15

Multi-horizon well performance forecasting with temporal fusion transformers

Maldonado-Cruz, E., & Pyrcz, M. J. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.101776 · Cited by 13

ShapTime: A General XAI Approach for Explainable Time Series Forecasting

Zhang, Y., Sun, Q., Qi, D., Liu, J., Ma, R., & Petrosian, O. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47721-8_45 · Cited by 13

Combination prediction of underground mine rock drilling time based on seasonal and trend decomposition using Loess

Nin, L., Liu, D., Wang, L., Ye, H., Wang, Q., Yan, D., & Zhao, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108064 · Cited by 13

Forking Uncertainties: Reliable Prediction and Model Predictive Control With Sequence Models via Conformal Risk Control

Zecchin, M., Park, S., & Simeone, O. (2024) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2024.3368229 · Cited by 21

CEEMD-based Multivariate Financial Time Series Forecasting using a Temporal Fusion Transformer

Ho, R. H., & Hung, K. (2024) · IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) · doi:10.1109/iscaie61308.2024.10576340 · Cited by 13

A Deep Learning Framework for Net Load Forecasting With Unsupervised Behind-the-Meter Disaggregated Data

Thepprom, C., Nupairoj, N., & Vateekul, P. (2024) · IEEE Access · doi:10.1109/access.2024.3425590 · Cited by 18

TFEformer: Temporal Feature Enhanced Transformer for Multivariate Time Series Forecasting

Ying, C., & Lu, J. (2024) · IEEE Access · doi:10.1109/access.2024.3480953 · Cited by 16

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting

Jafari, A., Fox, G., Rundle, J. B., Donnellan, A., & Ludwig, L. G. (2024) · GeoHazards · doi:10.3390/geohazards5040059 · Cited by 13

Temporal fusion point-interval forecasting: A comprehensive approach for financial time series prediction

Qi, X.-H., Xu, Z.-Y., & Wang, F.-H. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112600 · Cited by 15

An early prediction model on systemic risk under global risk: Using FinBERT and temporal fusion transformer to multimodal data fusion framework

Jin, X., & Lin, S.-L. (2025) · The North American Journal of Economics and Finance · doi:10.1016/j.najef.2025.102361 · Cited by 13

Smart home energy prediction framework using temporal Kolmogorov-Arnold transformer

Lu, Y., Vijayananth, V., & Perumal, T. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115529 · Cited by 13

A new perspective on non-ferrous metal price forecasting: An interpretable two-stage ensemble learning-based interval-valued forecasting system

Yang, W., Zhang, H., Wang, J., & Hao, Y. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103267 · Cited by 15

Extended-range forecasting of stream water temperature with deep-learning models

Padrón, R. S., Zappa, M., Bernhard, L., & Bogner, K. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-1685-2025 · Cited by 13

A Framework for User Traffic Prediction and Resource Allocation in 5G Networks

Konstantoulas, I., Loi, I., Tsimas, D., Sgarbas, K., Γκάμας, Α., & Bouras, C. (2025) · Applied Sciences · doi:10.3390/app15137603 · Cited by 13

Probabilistic hierarchical interpolation and interpretable neural network configurations for flood prediction

Saberian, M., Samadi, V. S., & Popescu, I. (2026) · Hydrology and earth system sciences · doi:10.5194/hess-30-371-2026 · Cited by 13

Advances in Time Series Forecasting Development for Power Systems’ Operation with MLOps

Gürses-Tran, G., & Monti, A. (2022) · Forecasting · doi:10.3390/forecast4020028 · Cited by 13

A joint Bayesian spatiotemporal risk prediction model of COVID-19 incidence, IC admission, and death with application to Sweden

Jaya, I. G. N. M., Folmer, H., & Lundberg, J. (2022) · The Annals of Regional Science · doi:10.1007/s00168-022-01191-1 · Cited by 16

New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements

Budennyy, S., Kazakov, A., Kovtun, E., & Евгеньевич, Ж. Л. (2023) · Scientific Reports · doi:10.1038/s41598-023-39301-4 · Cited by 16

Eigen: End-to-End Resource Optimization for Large-Scale Databases on the Cloud

Li, J. Y., Zhang, J., Zhou, W., Liu, Y., Zhang, S., Xue, Z., Xu, D., Fan, H. T., Zhou, F., & Li, F. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611540.3611565 · Cited by 13

Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach

Würfel, M., Han, Q., & Kaiser, M. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9672010 · Cited by 18

Monitoring Network Telescopes and Inferring Anomalous Traffic Through the Prediction of Probing Rates

Zakroum, M., François, J., Chrisment, I., & Ghogho, M. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3183497 · Cited by 14

Robust Auto-Scaling with Probabilistic Workload Forecasting for Cloud Databases

Hang, H., Tang, X., Sun, J., Bao, L., Lo, D., & Wang, H. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00308 · Cited by 12

A review of generative AI in aquaculture: Applications, case studies and challenges for smart and sustainable farming

Akram, W., Din, M. U., Saoud, L. S., & Hussain, I. (2025) · Aquacultural Engineering · doi:10.1016/j.aquaeng.2025.102637 · Cited by 19

Intelligent load forecasting technologies for diverse scenarios in the new power systems

Liu, T., Li, F., Zhang, D., Shan, L., Zhu, H., Du, P., Jiang, M., Goh, H. H., Kurniawan, T. A., Huang, C., et al. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116366 · Cited by 13

High-Dimensional Feature Selection for Power System Congestion Event Prognosis With Enhanced Evolutionary Computation

Xu, J. B., Jiang, X., Liao, S., Ke, D., Sun, Y., & Yao, L. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3260871 · Cited by 12

Graph neural networks for deep portfolio optimization

Ekmekcioğlu, Ö., & Pınar, M. Ç. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08862-w · Cited by 13

Crude oil price prediction using temporal fusion transformer model

He, K., Zheng, L., Yang, Q., Wu, D. C. W., Yu, Y., & Zou, Y. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.08.070 · Cited by 12

Explainable forecasting of global horizontal irradiance over multiple time steps using temporal fusion transformer

Mouloud, L. A., Kheldoun, A., Deboucha, A., & Mekhilef, S. (2023) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0159899 · Cited by 12

TADNet: Temporal Attention Decomposition Networks for Probabilistic Energy Forecasting

Ye, J., Zhao, B., Liu, D., Wei, Q., & Wang, Y. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3380388 · Cited by 12

Gas turbine prognostics via Temporal Fusion Transformer

Fentaye, A. D., & Kyprianidis, K. (2024) · The Aeronautical Journal · doi:10.1017/aer.2024.40 · Cited by 12

FMM-VMD-Transformer: A hybrid deep learning model for predicting natural gas consumption

Li, X., Zou, X., Cheng, J., Tang, M., & Hu, P. (2024) · Digital engineering. · doi:10.1016/j.dte.2024.100005 · Cited by 12

Separating the predictable part of returns with CNN-GRU-attention from inputs to predict stock returns

Yang, J., Zhang, M., Fang, R., Zhang, W., & Zhou, J. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112116 · Cited by 15

GIVT: Generative Infinite-Vocabulary Transformers

Tschannen, M., Eastwood, C., & Mentzer, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72998-0_17 · Cited by 46

Interpretable Tourism Demand Forecasting with Two-Stage Decomposition and Temporal Fusion Transformers

Wu, B., Wang, L., & Zeng, Y. (2024) · Journal of Systems Science and Complexity · doi:10.1007/s11424-024-2307-0 · Cited by 12

Enhancing Hierarchical Sales Forecasting with Promotional Data: A Comparative Study Using ARIMA and Deep Neural Networks

Teixeira, M., Oliveira, J. M., & Ramos, P. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6040128 · Cited by 15

Interpretable water level forecaster with spatiotemporal causal attention mechanisms

Hong, S., Choi, Y., & Jeon, J. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.10.003 · Cited by 12

Temporal fusion transformer model for predicting differential pressure in reverse osmosis process

Lee, S., Shim, J., Lee, J., Sung, H.-C., Lee, C., & Cho, K. H. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2024.106914 · Cited by 12

Transformer-based probabilistic forecasting of daily hotel demand using web search behavior

Rojas, C., & Jatowt, A. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.112966 · Cited by 15

A review of recent hybridized machine learning methodologies for time series forecasting on water-related variables

Koh, V. K. Z., Li, Y., Kek, X. Y., Shafiee, E., Lin, Z., & Wen, B. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.132909 · Cited by 12

Future prediction on the remaining useful life of proton exchange membrane fuel using temporal fusion transformer model

Chou, J., & Wang, F. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.123019 · Cited by 12

Forecasting Sales in Live-Streaming Cross-Border E-Commerce in the UK Using the Temporal Fusion Transformer Model

Zhang, Q., Li, X., & Gao, P. (2025) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer20020092 · Cited by 12

ChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis

Huang, W., Wei, W., He, X., Zhan, B., Xie, X., Zhang, M., Lai, S., Yuan, Z., Lai, J., Chen, R., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/74423 · Cited by 11

Bridging Self-Attention and Time Series Decomposition for Periodic Forecasting

Jiang, S., Syed, T., Zhu, X., Levy, J., Aronchik, B., & Sun, Y. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557077 · Cited by 12

Digital Twin of Atmospheric Environment: Sensory Data Fusion for High-Resolution PM2.5 Estimation and Action Policies Recommendation

Abutalip, K., Al-lahham, A., & Saddik, A. E. (2023) · IEEE Access · doi:10.1109/access.2023.3236414 · Cited by 12

Early Warning Software for Emergency Department Crowding

Tuominen, J., Koivistoinen, T., Kanniainen, J., Oksala, N., Palomäki, A., & Roine, A. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01958-9 · Cited by 12

Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series

Aryal, S., Nadarajah, D., Rupasinghe, L., Jayawardena, C., & Kasthurirathna, D. (2020) · Journal of Computer Science · doi:10.3844/jcssp.2020.1401.1416 · Cited by 18

Advancing Seaweed Cultivation: Integrating Physics Constraint in Machine Learning for Enhanced Biomass Forecasting in IMTA Systems

Kunapinun, A., Fairman, W., Wills, P. S., Hanisak, D., & Ouyang, B. (2024) · Remote Sensing · doi:10.3390/rs16234418 · Cited by 12

iTransformer-FFC: A Frequency-Aware Transformer Framework for Multi-Scale Time Series Forecasting

Tang, Y., & Cai, Z. (2025) · Electronics · doi:10.3390/electronics14122378 · Cited by 12

A GARCH-temporal fusion transformer model for the volatility prediction of exchange traded funds

Petrosino, L., Bacco, L., Salvati, G., Merone, M., & Papi, M. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11468-z · Cited by 11

Interpretable transformer based intra-day solar forecasting with spatiotemporal satellite and numerical weather prediction inputs

Chen, S., Jing, T., Li, M., Lee, H. H., Lam, M. C., & Bu, S. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100667 · Cited by 11

GCMNet: A global context Mamba network for long-term time series forecasting

Liu, X., Ren, J., Zhang, H., & Zhang, E. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113287 · Cited by 11

Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System

Gokhale, G., Gompel, J. V., Claessens, B., & Develder, C. (2023) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3600100.3626635 · Cited by 11

Replacing self-attentions with convolutional layers in multivariate long sequence time-series forecasting

Wang, Y., Peng, J., Wang, X., Zhang, Z., & Duan, J. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05205-z · Cited by 12

Retail Demand Forecasting: A Multivariate Approach and Comparison of Boosting and Deep Learning Methods

Theodoridis, G., & Tsadiras, A. K. (2024) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213024500015 · Cited by 15

AutoCyclic: Deep Learning Optimizer for Time Series Data Prediction

Arthur, C., Yudistira, N., & Dewi, C. (2024) · IEEE Access · doi:10.1109/access.2024.3356553 · Cited by 11

Temporal Implicit Multimodal Networks for Investment and Risk Management

Ang, G., & Lim, E. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3643855 · Cited by 12

Data pipeline for real-time energy consumption data management and prediction

Im, J., Lee, J.-K., Lee, S., & Kwon, H.-Y. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1308236 · Cited by 17

Multiple-Input–Multiple-Output Randomized Fuzzy Cognitive Map Method for High-Dimensional Time Series Forecasting

Orang, O., Bitencourt, H. V., de Souza, L. A. F., Lucas, P. D. O. E., Silva, P. C. D. L. E., & Guimarães, F. G. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3379853 · Cited by 13

A hierarchical deep model integrating economic facts for stock movement prediction

Yang, J., Zhang, M., Feng, S., Zhang, X., & Bai, X. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108320 · Cited by 12

Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration

Huan, J., Deng, L., Zhu, Y., Jiang, S., & Qi, F. (2024) · Energies · doi:10.3390/en17102395 · Cited by 13

A bayesian spatio-temporal dynamic analysis of food security in Africa

Bofa, A., & Zewotir, T. T. (2024) · Scientific Reports · doi:10.1038/s41598-024-65989-z · Cited by 11

Temporal Fusion Transformers for Enhanced Multivariate Time Series Forecasting of Indonesian Stock Prices

Hartanto, S., & Gunawan, A. A. S. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150713 · Cited by 11

AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting

Fischer, R., & Saadallah, A. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672057 · Cited by 11

Long-Term Demand Prediction for Public Bicycle Sharing System: A Spatio-Temporal Attentional Graph Convolution Networks Approach

Liu, Z., Gokon, H., & Sekimoto, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3469169 · Cited by 11

Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting

Gorshenin, A. K., & Vilyaev, A. L. (2024) · AI · doi:10.3390/ai5040097 · Cited by 14

Transformers and attention-based networks in quantitative trading: a comprehensive survey

Silva, L. C. E., Fonseca, G. D. F., & de Castro, P. A. L. (2024) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3677052.3698684 · Cited by 12

Trustworthy Artificial Intelligence in the Energy Sector: Landscape Analysis and Evaluation Framework

Pelekis, S., Karakolis, E., Lampropoulos, G., Mouzakitis, S., Markaki, O. I., Ntanos, C., & Askounis, D. (2024) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc61926.2024.10794222 · Cited by 14

Explainable Soybean Futures Price Forecasting Based on Multi‐Source Feature Fusion

Wu, B., Yu, S., & Lv, S.-X. (2024) · Journal of Forecasting · doi:10.1002/for.3246 · Cited by 11

Time Series Methods and Business Intelligent Tools for Budget Planning—Case Study

Grobler-Dębska, K., Mularczyk, R., Gawęda, B., & Kucharska, E. (2024) · Applied Sciences · doi:10.3390/app15010287 · Cited by 11

Forecasting PV Energy Generation Using Transformer-Based Architectures: A Comparative Study of Lag-Llama, TFT, and DeepAR

Amjad, F., Korõtko, T., & Rosin, A. (2024) · IEEE International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) · doi:10.1109/rtucon62997.2024.10830763 · Cited by 11

A Novel Multi-Scale Time Fusion Transformer for Long-Range Spectrum Occupancy Prediction

Li, S., Sun, Y., Yue, W., Yao, M., Han, Y. L., Gui, G., Lin, Y., & Xiang, W. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3540920 · Cited by 11

Dual-gate Temporal Fusion Transformer for estimating large-scale land surface solar irradiation

Liao, X., Wong, M. S., & Zhu, R. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115510 · Cited by 11

An end-to-end explainability framework for spatio-temporal predictive modeling

Altieri, M., Ceci, M., & Corizzo, R. (2025) · Machine Learning · doi:10.1007/s10994-024-06733-6 · Cited by 11

An interpretable machine learning model for seasonal precipitation forecasting

Pinheiro, E., & Ouarda, T. B. M. J. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02207-2 · Cited by 16

Traffic forecasting with meta attentive graph convolutional recurrent network

Zeb, A., Zheng, J., Ye, Y., Chen, J., Zhang, S., Wei, X., & Yu, J. J. Q. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128073 · Cited by 13

Dynamic Dual-Phase Forecasting Model for New Product Demand Using Machine Learning and Statistical Control

Wang, C.-C. (2025) · Mathematics · doi:10.3390/math13101613 · Cited by 11

Interpretable and sensorless lithium-ion battery temperature estimation: A Temporal Fusion Transformer approach

Ma, M., Xu, Q., Chen, Q., Jiang, T., & Wang, H. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.117414 · Cited by 11

Deep Learning and Transformer Models for Groundwater Level Prediction in the Marvdasht Plain: Protecting UNESCO Heritage Sites—Persepolis and Naqsh-e Rustam

Heidarian, P., López, F. P. A., Tan, Y., Firozjaee, S. F., Yousefi, T., Salehi, H., Pour, A. O., Marca, M. E. O., Zhou, G., Azhdari, A., et al. (2025) · Remote Sensing · doi:10.3390/rs17142532 · Cited by 11

A self-adaptive physics-informed neural network for real-time prediction of structural damage

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