Cellular traffic prediction with machine learning: A survey
W Jiang - Expert Systems with Applications, 2022 - Elsevier
Cellular networks are important for the success of modern communication systems, which
support billions of mobile users and devices. Powered by artificial intelligence techniques …
support billions of mobile users and devices. Powered by artificial intelligence techniques …
Graph-guided network for irregularly sampled multivariate time series
In many domains, including healthcare, biology, and climate science, time series are
irregularly sampled with varying time intervals between successive readouts and different …
irregularly sampled with varying time intervals between successive readouts and different …
Short-term wind power forecasting based on multivariate/multi-step LSTM with temporal feature attention mechanism
X Liu, J Zhou - Applied Soft Computing, 2024 - Elsevier
Precision enhancement for short-term wind power forecasting can alleviate negative impact
of the forecasting results on wind power generation. Due to complexities and nonlinearities …
of the forecasting results on wind power generation. Due to complexities and nonlinearities …
Deep Learning on Network Traffic Prediction: Recent Advances, Analysis, and Future Directions
From the perspective of telecommunications, next-generation networks or beyond 5G will
inevitably face the challenge of a growing number of users and devices. Such growth results …
inevitably face the challenge of a growing number of users and devices. Such growth results …
A solar forecasting framework based on federated learning and distributed computing
Solar forecasting is a crucial and cost-effective tool for better utilization of solar energy for
smart environment design. Artificial intelligence (AI) technologies, such as machine learning …
smart environment design. Artificial intelligence (AI) technologies, such as machine learning …
Rethinking modeling Alzheimer's disease progression from a multi-task learning perspective with deep recurrent neural network
Alzheimer's disease (AD) is a severe neurodegenerative disorder that usually starts slowly
and progressively worsens. Predicting the progression of Alzheimer's disease with …
and progressively worsens. Predicting the progression of Alzheimer's disease with …
Multi-task time series forecasting based on graph neural networks
X Han, Y Huang, Z Pan, W Li, Y Hu, G Lin - Entropy, 2023 - mdpi.com
Accurate time series forecasting is of great importance in real-world scenarios such as
health care, transportation, and finance. Because of the tendency, temporal variations, and …
health care, transportation, and finance. Because of the tendency, temporal variations, and …
Ddmt: Denoising diffusion mask transformer models for multivariate time series anomaly detection
C Yang, T Wang, X Yan - arxiv preprint arxiv:2310.08800, 2023 - arxiv.org
Anomaly detection in multivariate time series has emerged as a crucial challenge in time
series research, with significant research implications in various fields such as fraud …
series research, with significant research implications in various fields such as fraud …
MTL-Deep-STF: A multitask learning based deep spatiotemporal fusion model for outdoor air temperature prediction in building HVAC systems
D Qiao, B Shen, X Dong, H Zheng, W Song… - Journal of Building …, 2022 - Elsevier
Buildings consume large quantities of energy. Reducing building energy consumption is
essential to achieving carbon neutrality goals. Building energy consumption is strongly …
essential to achieving carbon neutrality goals. Building energy consumption is strongly …
A Survey on Deep Learning for Cellular Traffic Prediction
X Wang, Z Wang, K Yang, Z Song, C Bian… - Intelligent …, 2024 - spj.science.org
With the widespread deployment of 5G networks and the proliferation of mobile devices,
mobile network operators are confronted not only with massive data growth in mobile traffic …
mobile network operators are confronted not only with massive data growth in mobile traffic …