Results 71 to 80 of about 18,233,102 (295)

Multivariate Financial Time-Series Prediction With Certified Robustness

IEEE Access, 2020
The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention.
Hui Li, Yunpeng Cui, Shuo Wang, Juan Liu, Jinyuan Qin, Yilin Yang   +5 more
doaj   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting

Tsinghua Science and Technology
Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields.
Zhuolun Jiang, Zefei Ning, Hao Miao, Li Wang   +3 more
doaj   +1 more source

UDP‐Glucose‐6‐Dehydrogenase Mediated O‐GlcNAcylation of Tight Junction Protein 1 Suppresses Metastasis in Renal Cell Carcinoma

Advanced Science, EarlyView.
Our research unveiled a regulatory paradigm wherein TRIM25 orchestrates the ubiquitin‐mediated degradation of UGDH. UGDH modulates the protein stability of TJP1 by regulating O‐GlcNAcylation levels, effectively impeding the metastasis of ccRCC. Our insights elevate UGDH to a pivotal biomarker and tumor suppressor, marking the first demonstration that ...
Xiaolin Chen, Yuhang Bao, Tao Liu, Jianhui Qiu, Yuanyuan Ma, Zedan Zhang, Chuandong Wang, Ruiyi Deng, Zheng Song, Tianyi He, Haode Liu, Jingcheng Zhou, Yizhou Wang, Kan Gong   +13 more
wiley   +1 more source

Frequent State Transition Patterns of Multivariate Time Series

IEEE Access, 2019
Sequence pattern discovery is a key issue in multivariate time series analysis. Popular approaches first obtain the pattern of each single-variate time series and then obtain cross-variate associations.
Zhi-Heng Zhang, Fan Min
doaj   +1 more source

Robust exponential smoothing of multivariate time series. [PDF]

Multivariate time series may contain outliers of different types. In presence of such outliers, applying standard multivariate time series techniques becomes unreliable. A robust version of multivariate exponential smoothing is proposed.
Croux, Christophe, Gelper, Sarah, Mahieu, Koen   +2 more
core  

Integrative Multi‐Omics Analysis Reveals a Mitochondrial–Immune Axis Associated With Neoadjuvant Chemotherapy Response in High‐Grade Serous Ovarian Cancer

Advanced Science, EarlyView.
Integrative multi‐omics analysis delineates a mitochondrial–immune axis governing neoadjuvant chemotherapy response in high‐grade serous ovarian cancer. Immune‐active tumors exhibit enhanced B‐cell infiltration and favorable sensitivity, whereas metabolically rewired tumors display oxidative phosphorylation dependency and resistance.
Wei Jiang, Yi Wang, Xiaohang Lu, Qianlan Yao, Libing Xiang, Xu Cai, Xiaoyu Tu, Shaoxian Tang, Ying Xu, Yufan Cheng, Liming Shen, Huijuan Yang   +11 more
wiley   +1 more source

DiffTST: Diff Transformer for Multivariate Time Series Forecast

IEEE Access
Deep learning models employing the Transformer architecture have demonstrated exceptional performance in the field of multivariate time series forecasting research.
Song Yang, Wenyong Han, Yaping Wan, Tao Zhu, Zhiming Liu, Shuangjian Li   +5 more
doaj   +1 more source

Robust online signal extraction from multivariate time series [PDF]

We introduce robust regression-based online filters for multivariate time series and discuss their performance in real time signal extraction settings. We focus on methods that can deal with time series exhibiting patterns such as trends, level changes ...
Gather, Ursula, Lanius, Vivian
core  

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source