Results 31 to 40 of about 18,244,857 (290)

Forecasting Video QoE With Deep Learning From Multivariate Time-Series

IEEE Open Journal of Signal Processing, 2021
The end users’ satisfactory Quality of Experience (QoE) is a fundamental criterion for networked video service providers such as video-on-demand providers (Netflix, YouTube, etc.), cloud gaming providers (Google Stadia, PlayStation Now, etc.) and ...
Hossein Ebrahimi Dinaki, Shervin Shirmohammadi, Emil Janulewicz, David Cote   +3 more
doaj   +1 more source

Multi-step CNN forecasting for COVID-19 multivariate time-series

IJAIN (International Journal of Advances in Intelligent Informatics), 2023
The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced.
Haviluddin Haviluddin, Rayner Alfred
doaj   +1 more source

Multivariate Dynamic Kernels for Financial Time Series Forecasting [PDF]

, 2016
We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple
Peña Grass, Mauricio, Arratia Quesada, Argimiro Alejandro, Belanche Muñoz, Luis Antonio   +2 more
openaire   +2 more sources

Post Constraint and Correction: A Plug-and-Play Module for Boosting the Performance of Deep Learning Based Weather Multivariate Time Series Forecasting

Applied Sciences
Weather forecasting is essential for various applications such as agriculture and transportation, and relies heavily on meteorological sequential data such as multivariate time series collected from weather stations.
Zhengrui Wang, Zhongwen Luo, Zirui Yang, Yuanyuan Liu   +3 more
doaj   +1 more source

ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs

Machine Learning and Knowledge Extraction
Large language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models.
Jinglei Pei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu, Kang Qin   +5 more
doaj   +1 more source

Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting

CoRR, 2020
Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models. Previous interpretation methods, including the methods for general neural networks and attention-based methods, mainly consider
Qingyi Pan, Wenbo Hu 0001, Jun Zhu 0001
openaire   +3 more sources

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source

Accurate and noninvasive prostate cancer detection using plasma‐derived extracellular vesicle RNA

FEBS Open Bio, EarlyView.
Plasma extracellular vesicles were captured with WGA‐conjugated magnetic beads and profiled for RNA biomarkers. A three‐RNA panel (NM_024955, NR_047469, and NR_002564) distinguished prostate cancer from healthy controls and benign prostatic hyperplasia, supporting a simple, noninvasive approach to improve prostate cancer detection.
Hanping Wei, Haoran Wu, Wei Feng
wiley   +1 more source

Relationship Between Neurologic Symptoms and Signs and FMR1 Genotype in Premutation Carriers

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background and Objectives Fragile X‐associated Tremor/Ataxia Syndrome (FXTAS) is the most severe late‐onset condition caused by a premutation in the FMR1 gene, characterized by expanded CGG triplet repeats of 55–200. Clinical presentations of FXTAS, including gait ataxia, kinetic tremor, cognitive decline, and rare Parkinsonism, are linked to ...
Flora Tassone, Freddy Chafota, Miguel E. Rentería, Randi J. Hagerman, Ellery Santos, Anna Atkinson, Nicholas G. Martin, Elsdon Storey, Danuta Z. Loesch   +8 more
wiley   +1 more source

Learning short multivariate time series models through evolutionary and sparse matrix computation [PDF]

, 2005
Multivariate time series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is important for many decision making activities.
Liu, X, Kok, J, Swift, S
core   +1 more source