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

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

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707

"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

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)

Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513

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

Self-Consistent Learning of Neural Dynamical Systems From Noisy Time Series

Wang, Z., & Guet, C. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3146332 · Cited by 11

Research on the data validity of a coal mine solid backfill working face sensing system based on an improved transformer

Lei, B. W., Yang, S., Liu, Y., Wang, Y., & Zhang, Z. (2023) · Scientific Reports · doi:10.1038/s41598-023-38365-6 · Cited by 11

Short-term load forecasting based on the improved bi-directional long short-term memory model

Jiang, W., & Xie, X. (2026) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2026.111789 · Cited by 10

Explainable time-series forecasting with sampling-free SHAP for Transformers

Hertel, M., Pütz, S., Mikut, R., Hagenmeyer, V., & Schäfer, B. (2026) · Nature Communications · doi:10.1038/s41467-026-73243-5 · Cited by 13

GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks

Buczyński, M., & Chlebuś, M. (2023) · Computational Economics · doi:10.1007/s10614-023-10390-7 · Cited by 13

Predicting combustion behavior in rotating detonation engines using an interpretable deep learning method

Shen, D., Sheng, Z., Zhang, Y., Rong, G., Wu, K., & Wang, J. (2023) · Physics of Fluids · doi:10.1063/5.0155991 · Cited by 11

Towards Trustworthy and Understandable AI: Unraveling Explainability Strategies on Simplifying Algorithms, Appropriate Information Disclosure, and High-level Collaboration

Yu, S. (2023) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3616961.3616965 · Cited by 16

MetaSTNet: Multimodal Meta-Learning for Cellular Traffic Conformal Prediction

Ma, H., & Yang, K. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3335626 · Cited by 12

Deep temporal modelling of clinical depression through social media text

Farruque, N., Goebel, R. G., Sivapalan, S., & Zai͏̈ane, O. R. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100052 · Cited by 14

Forecasting environmental water availability of lakes using temporal fusion transformer: case studies of China’s two largest freshwater lakes

Huang, F., Ochoa, C. G., Li, Q., Shen, X., Qian, Z., Han, S., Zhang, N., & Yu, M. (2024) · Environmental Monitoring and Assessment · doi:10.1007/s10661-024-12331-9 · Cited by 10

Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Long, K., Fei, Y., Xing, L., Pei, X., Yao, D., Zheng, O., & Abdel-Aty, M. A. (2024) · Transportmetrica B Transport Dynamics · doi:10.1080/21680566.2024.2326018 · Cited by 10

Learning evolving relations for multivariate time series forecasting

Nguyen-Thai, B., Le, V., Tieu, N.-D. T., Tran, T., Venkatesh, S., & Ramzan, N. (2024) · Applied Intelligence · doi:10.1007/s10489-023-05220-0 · Cited by 12

Enhancing pore pressure prediction accuracy: A knowledge-driven approach with temporal fusion transformer

Cao, S., Wang, C. C. Q., Niu, Q., Zheng, Q., Shen, G., Chen, B., Shi, X., & Zhang, W. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.212839 · Cited by 10

Optimization of the Use of Cloud Computing Resources Using Exploratory Data Analysis and Machine Learning

Nawrocki, P., & Smendowski, M. (2024) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2024-0016 · Cited by 10

Modeling group-level public sentiment in social networks through topic and role enhancement

Zhang, R., Liu, B., Cao, J., Zhao, H., Sun, X., Liu, Y., & Sun, X. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112594 · Cited by 10

Time-mixing and feature-mixing modelling for realized volatility forecast: Evidence from TSMixer model

Souto, H. G., Heuvel, S. K., & Louzada, F. (2024) · The Journal of Finance and Data Science · doi:10.1016/j.jfds.2024.100143 · Cited by 17

Towards Accurate Flood Predictions: A Deep Learning Approach Using Wupper River Data

Hahn, Y., Kienitz, P., Wönkhaus, M., Meyes, R., & Meisen, T. (2024) · Water · doi:10.3390/w16233368 · Cited by 10

Adaptive transformer-based multi-task learning framework for synchronous prediction of substation flooding and outage risks

Shi, Y., Shi, Y., Yao, D., Lu, M., & Liang, Y. (2025) · Electric Power Systems Research · doi:10.1016/j.epsr.2025.111450 · Cited by 12

Aircraft dynamics modeling at high angle of attack incorporating residual transformer autoencoder and physical mechanisms

Ma, J., Zhu, Q., Xue, T., Ai, J., & Dong, Y. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110045 · Cited by 10

A Transformer-based fault detection method built on real-time data from microreactors

Chen, F., Zhu, Z., Qv, F., Ni, L., Jiang, J., & Chen, Z. (2025) · Chemical Engineering Science · doi:10.1016/j.ces.2025.121712 · Cited by 10

An interpretable temperature prediction method for grain in storage based on improved temporal Fusion Transformers

Li, X., Wu, W., Guo, H., Qiao, X., Lu, Y., Wu, Y. J., & Qiao, G. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110414 · Cited by 12

Time Series Forecasting of Natural Inflow in Hydroelectric Power Plants Using Hyper‐Tuned Temporal Fusion Transformer With Hodrick–Prescott Filter

Muniz, R. N., Buratto, W. G., Nied, A., Cardoso, R., Finardi, E. C., & González, G. V. (2025) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.70087 · Cited by 10

TimeGPT-based multi-step-ahead key quality indicator forecasting for industrial processes

Zhang, X., Chen, Y., He, B., Song, Z., & Kano, M. (2025) · Control Engineering Practice · doi:10.1016/j.conengprac.2025.106410 · Cited by 10

Hybrid Hydrological Forecasting Through a Physical Model and a Weather-Informed Transformer Model: A Case Study in Greek Watershed

Ampas, H., Refanidis, I., & Ampas, V. (2025) · Applied Sciences · doi:10.3390/app15126679 · Cited by 10

Bridging Traditional-Statistics and Machine-Learning Approaches in Psychology: Navigating Small Samples, Measurement Error, Nonindependent Observations, and Missing Data

Lavelle-Hill, R. E., Smith, G., & Murayama, K. (2025) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459251345696 · Cited by 11

FTMixer: Frequency and Time Domain Representations Fusion for Time Series Forecasting

Li, Z., Tan, Y., Cheng, X., & Qin, Y. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3594595 · Cited by 24

Time Series Analysis from Classical Methods to Transformer-Based Approaches: A Review

Thundiyil, S., & Picone, J. (2025) · Springer eBooks · doi:10.1007/978-3-031-88024-7_2 · Cited by 10

Comparative study of daily streamflow prediction based on coupling SWAT+ with interpretable machine learning algorithms

Cao, C., & Ying, M. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103406 · Cited by 10

Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)

Dey, B., Zhao, D., Andrews, B. H., Newman, J. A., Izbicki, R., & Lee, A. B. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ae1f05 · Cited by 10

Vision on the bog: Cranberry crop risk evaluation with deep learning

Akiva, P., Planche, B., Roy, A., Oudemans, P. V., & Dana, K. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107444 · Cited by 11

A UUV target detection method based on Informer

Zhuo, C., Fan, X., Guo, L., & Cui, Y. (2022) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic57696.2022.10075304 · Cited by 10

Seismic prediction of porosity in tight reservoirs based on transformer

Su, Z., Cao, J., Xiang, T., Fu, J., & Shi, S. (2023) · Frontiers in Earth Science · doi:10.3389/feart.2023.1137645 · Cited by 11

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

eslamieh, P., Shajari, M., & Nickabadi, A. (2023) · Mathematics · doi:10.3390/math11132950 · Cited by 10

A bearing fault detection and remaining useful life prediction method based on a multi-branch residual feature fusion mechanism and optimized weight allocation

Yao, Y., Liang, T., Tan, J., & Jing, Y. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/ad0b67 · Cited by 10

Wearables and Detection of Falls: A Comparison of Machine Learning Methods and Sensors Positioning

Pinto, A. B. A., de Assis, G. A., Torres, L. C. B., Beltrame, T., & Domingues, D. M. G. (2022) · Neural Processing Letters · doi:10.1007/s11063-021-10724-2 · Cited by 10

Virtual-diagnostic-based time stamping for ultrafast electron diffraction

Cropp, F., Moos, L., Scheinker, A., Gilardi, A., Wang, D. J., Paiagua, S., Serrano, C., Musumeci, P., & Filippetto, D. (2023) · Physical Review Accelerators and Beams · doi:10.1103/physrevaccelbeams.26.052801 · Cited by 10

A Neural Phillips Curve and a Deep Output GapPreprint

Coulombe, P. G. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4018079 · Cited by 18

Deep Learning-Based Time Series Forecasting Models Evaluation for the Forecast of Chlorophyll a and Dissolved Oxygen in the Mar Menor

López-Andreu, F. J., López-Morales, J. A., Hernández-Guillen, Z., Carrero-Rodrigo, J. A., Sánchez-Alcaraz, M., Atenza, J. F., & Erena, M. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11071473 · Cited by 14

An acoustic emission onset time determination method based on Transformer

Yang, Z., Li, H., & Chen, R. (2024) · Structural Health Monitoring · doi:10.1177/14759217231223078 · Cited by 9

Identification of Algal Blooms in Lakes in the Baltic States Using Sentinel-2 Data and Artificial Neural Networks

Grendaitė, D., & Petkevičius, L. (2024) · IEEE Access · doi:10.1109/access.2024.3366491 · Cited by 9

Multimodal Temporal Fusion Transformers are Good Product Demand Forecasters

Sukel, M., Rudinac, S., & Worring, M. (2024) · IEEE Multimedia · doi:10.1109/mmul.2024.3373827 · Cited by 11

Temporal feature decomposition fusion network for building energy multi-step prediction

Yang, Y., Fu, Q., Chen, J., Lu, Y., Wang, Y., & Wu, H. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.110034 · Cited by 9

A case study of spatiotemporal forecasting techniques for weather forecasting

Sofi, S. S., & Oseledets, I. V. (2024) · GeoInformatica · doi:10.1007/s10707-024-00530-y · Cited by 14

Multidimensional dynamic attention for multivariate time series forecasting

Almaghrabi, S., Rana, M., Hamilton, M., & Rahaman, M. S. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112350 · Cited by 9

Walking Back the Data Quantity Assumption to Improve Time Series Prediction in Deep Learning

Lazcano, A., Hidalgo, P., & Sandubete, J. E. (2024) · Applied Sciences · doi:10.3390/app142311081 · Cited by 13

Efficient deterministic renewable energy forecasting guided by multiple-location weather data

Symeonidis, C., & Nikolaidis, N. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10607-2 · Cited by 9

Enhancing Short-Term Load Forecasting Through K-Shape Clustering and Deep Learning Integration

Zhang, W., Cheng, M., Xiang, Q., & Li, Q. (2025) · IEEE Access · doi:10.1109/access.2025.3541574 · Cited by 10

An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting

Cao, R., & Wang, Q. (2024) · IEEE International Conference on Future Machine Learning and Data Science (FMLDS) · doi:10.1109/fmlds63805.2024.00098 · Cited by 12

Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrations

Yu, R., Qiu, C., Ladwig, R., Hanson, P. C., Xie, Y., Li, Y., & Jia, X. (2024) · Proceedings · doi:10.1109/icdm59182.2024.00065 · Cited by 11

Enhancing Hydro-climatic and land parameter forecasting using Transformer networks

Subhadarsini, S., Kumar, D. N., & Govindaraju, R. S. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.132906 · Cited by 8

Uncertainty-Aware Self-Attention Model for Time Series Prediction with Missing Values

Li, J., Wang, C., Su, W., Ye, D., & Wang, Z. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9030181 · Cited by 9

Multi-source temporal attention fusion network (MTAFN) for driving risk assessment based on naturalistic driving data

Bai, C., Yang, C., Rong, D., Guo, W., Gao, X., Yao, W., & Jin, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127502 · Cited by 9

Simultaneous forecasting of vital sign trajectories in the ICU

He, R., & Chiang, J. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-99719-w · Cited by 9

Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments

Dash, R., Sinha, A., Reddy, K. J., Dhanamjayulu, C., & Kamwa, I. (2025) · IEEE Access · doi:10.1109/access.2025.3570719 · Cited by 9

Smoothing Techniques for Improving COVID-19 Time Series Forecasting Across Countries

Zbezhkhovska, U., & Chumachenko, D. (2025) · Computation · doi:10.3390/computation13060136 · Cited by 9

LTR-Net: A deep learning-based approach for financial data prediction and risk evaluation in enterprises

Liu, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328013 · Cited by 9

Intelligent framework for bipolar membrane electrodialysis: AI-based forecasting and multi-objective optimization of electrochemical performance

Moon, J., Lee, S., Kim, J. H., & Cho, K. H. (2025) · Desalination · doi:10.1016/j.desal.2025.119271 · Cited by 9

Probabilistic forecasting of power system imbalance using neural network-based ensembles

Gompel, J. V., Claessens, B., & Develder, C. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126714 · Cited by 9

Hybrid CF-CNN-BiLSTM hypertuned by Bayesian optimization for thermal power generation and decarbonization forecasting

Buratto, W. G., Muniz, R. N., Nied, A., Barros, C. F. D. O., Finardi, E. C., & González, G. V. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.111199 · Cited by 9

Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany

Weißenborn, M., Breuer, L., & Houska, T. (2025) · Hydrology and earth system sciences · doi:10.5194/hess-29-5131-2025 · Cited by 10

Robust and efficient multihorizon photovoltaic power forecasting with a dilated multi-scale Transformer

Liu, Y., & Wang, Z. (2025) · Solar Energy · doi:10.1016/j.solener.2025.114077 · Cited by 9

Ensemble-based predictive analytics for demand forecasting in multi-channel retailing

Samal, T., & Ghosh, A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130212 · Cited by 16

Storm Surge Forecasting along Korea Strait Using Artificial Neural Network

Park, Y., Kim, E., Choi, Y., Seo, G., Kim, Y., & Kim, H. (2022) · Journal of Marine Science and Engineering · doi:10.3390/jmse10040535 · Cited by 9

A Novel Data-Driven Method for Medium-Term Power Consumption Forecasting Based on Transformer-LightGBM

Yang, G., Du, S., Duan, Q., & Su, J. (2022) · Mobile Information Systems · doi:10.1155/2022/5465322 · Cited by 9

Structural Forecasting for Short-Term Tropical Cyclone Intensity Guidance

McNeely, T., Khokhlov, P., Dalmasso, N., Wood, K. M., & Lee, A. B. (2023) · Weather and Forecasting · doi:10.1175/waf-d-22-0111.1 · Cited by 9

Wheat Yield Prediction using Temporal Fusion Transformers

Junankar, T., Sondhi, J. K., & Nair, A. M. (2023) · International Conference for Innovation in Technology (INOCON) · doi:10.1109/inocon57975.2023.10101144 · Cited by 11

Multi-view kernel PCA for time series forecasting

Pandey, A., Meulemeester, H. D., Moor, B. D., & Suykens, J. A. K. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126639 · Cited by 9

Long-Term Regional Influenza-Like-Illness Forecasting Using Exogenous Data

Papagiannopoulou, E., Bossa, M. N., Deligiannis, N., & Hichem Sahli, B. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3377529 · Cited by 8

RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data

Yeh, C.-C. M., Fan, Y., Dai, X., Saini, U. S., Lai, V., Aboagye, P. O., Wang, J.-P., Chen, H., Zheng, Y., Zhuang, Z., et al. (2024) · Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671881 · Cited by 33

Constructing Global Coherence Representations: Identifying Interpretability and Coherences of Transformer Attention in Time Series Data

Schwenke, L., & Atzmueller, M. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564126 · Cited by 9

Better Prevent than React: Deep Stratified Learning to Predict Hate Intensity of Twitter Reply Chains

Sahnan, D., Dahiya, S., Goel, V., Bandhakavi, A., & Chakraborty, T. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00066 · Cited by 9

A Transformer-Based Multi-Entity Load Forecasting Method for Integrated Energy Systems

Wang, Z., Zhu, Z., Xiao, G., Bai, B., & Zhang, Y. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.952420 · Cited by 9

TransCODE: Co-Design of Transformers and Accelerators for Efficient Training and Inference

Tuli, S., & Jha, N. K. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3283443 · Cited by 11

On Trustworthy Decision-Making Process of Human Drivers From the View of Perceptual Uncertainty Reduction

Wang, H., Liu, H., Wang, W., & Sun, L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3316455 · Cited by 10

A Concurrent CNN-RNN Approach for Multi-step Wind Power Forecasting

Kazmi, S. I. H. (2024) · doi:10.32920/26052718.v1 · Cited by 9

Temporal Fusion Transformer-Gaussian Process for Multi-Horizon River Level Prediction and Uncertainty Quantification

Wang, C., & Tang, W. (2023) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126623503097 · Cited by 8

Safety-Aware Vehicle Trajectory Prediction With Spatiotemporal Attentional GAN in Hybrid Transportation System

Huo, J., Wang, L., Wen, X., Liu, L., Yao, G., Lloret-Batlle, R., & Lu, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3327472 · Cited by 8

Dynamic weighted ensemble for diarrhoea incidence predictions

Do, T. D., Nguyen, T. D., Ta, V. C., Anh, D. T., Thi, T.-H. T., Phan, D., & Mai, S. T. (2023) · Machine Learning · doi:10.1007/s10994-023-06465-z · Cited by 8

Prediction of Currency Exchange Rate Based on Transformers

Zhao, L., & Yan, W. (2024) · Journal of risk and financial management · doi:10.3390/jrfm17080332 · Cited by 8

A Multi-Task Learning Framework for Underwater Acoustic Channel Prediction: Performance Analysis on Real-World Data

Tian, T., Raj, A., Xavier, B. M., Zhang, Y., Wu, F., & Yang, K. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3435018 · Cited by 8

Seamless and Multi-Resolution Energy Forecasting

Wang, C., Pinson, P., & Wang, Y. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3447162 · Cited by 8

Bilinear-DRTFT: Uncertainty prediction in electricity load considering multiple demand responses

Xu, M., Li, Q., Zhao, Z., & Sun, C. (2024) · Energy · doi:10.1016/j.energy.2024.133067 · Cited by 8

Probabilistic Forecasting With Modified N-BEATS Networks

Belle, J. V., Crevits, R., Caljon, D., & Verbeke, W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3450832 · Cited by 8

TFT-MPIR: An end-to-end multi-period inventory replenishment strategy based on temporal fusion transformer

Guo, W., Ren, Z.-M., Du, W., & Weng, T. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125464 · Cited by 8

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease

Bernal, E. A., Yang, S., Herbst, K., & Venuto, C. S. (2024) · Clinical and Translational Science · doi:10.1111/cts.70066 · Cited by 8

A Multitask Graph Convolutional Network With Attention-Based Seasonal-Trend Decomposition for Short-Term Load Forecasting

Zhang, W., Yu, Y., Ji, S., Zhang, S., & Ni, C. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3506832 · Cited by 8

Integrated Spatiotemporal Hybrid Solar PV Generation Forecast Between Countries on Different Continents Using Transfer Learning Method

Kim, B., Belkilani, K., Heilscher, G., Otto, M.-O., Huh, J.-S., & Suh, D. (2024) · IEEE Access · doi:10.1109/access.2024.3514098 · Cited by 8

Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model With Seasonal-Trend Decomposition

Boddu, Y., Manimaran, A., Balakrishnan, A., Sucharitha, M., & Babu, J. S. (2024) · IEEE Access · doi:10.1109/access.2024.3515095 · Cited by 8

An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model

Lenk, A., Vogt, M., & Herrmann, C. A. (2024) · Energies · doi:10.3390/en18010002 · Cited by 8

Bitcoin Ordinals: Bitcoin Price and Transaction Fee Rate Predictions

Wang, M., Braslavski, P., Manevich, V. A., & Ignatov, D. I. (2025) · IEEE Access · doi:10.1109/access.2025.3541302 · Cited by 8

A Comprehensive Comparative Study of Quick Invariant Signature (QIS), Dynamic Time Warping (DTW), and Hybrid QIS + DTW for Time Series Analysis

Shahbazkia, H. R., Khosravani, H. R., Pulatov, A., Hajimani, E., & Kiazadeh, M. (2025) · Mathematics · doi:10.3390/math13060999 · Cited by 8

Remaining Useful Life Prediction Method for Bearings Based on Pruned Exact Linear Time State Segmentation and Time–Frequency Diagram

Xu, W. M., Fan, J., Wang, H., & Cai, L. (2025) · Sensors · doi:10.3390/s25061950 · Cited by 8

Hierarchical neural additive models for interpretable demand forecasts

Feddersen, L., & Cleophas, C. (2025) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2025.03.003 · Cited by 15

On the Implementation of Temporal Fusion Transformers for Target Recognition and Tracking

Mylonakis, C. M., Velanas, P., & Zaharis, Z. D. (2025) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc61545.2025.10978748 · Cited by 8

An AI-Enhanced Forecasting Framework: Integrating LSTM and Transformer-Based Sentiment for Stock Price Prediction

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