Results 131 to 140 of about 234,460 (307)
PRICE FORECASTING WITH TIME-SERIES METHODS AND NONSTATIONARY DATA: AN APPLICATION TO MONTHLY U.S. CATTLE PRICES [PDF]
The forecasting performance of various multivariate as well as univariate ARIMA models is evaluated in the presence of nonstationarity. The results indicate the importance of identifying the characteristics of the time series by testing for types of ...
Zapata, Hector O., Garcia, Philip
core +1 more source
A gap‐free genome assembly and multi‐omics comparison of the terrestrial slug Laevichaulis alte with an aquatic relative reveal that expansion of the VEGF family orchestrates mucus production, lipid metabolism, and immune defense—highlighting key molecular innovations for conquering life on land.
Gang Wang +19 more
wiley +1 more source
On Multivariate Financial Time Series Classification
This article investigates the use of Machine Learning and Deep Learning models in multivariate time series analysis within financial markets. It compares small and big data approaches, focusing on their distinct challenges and the benefits of scaling. Traditional methods such as SVMs are contrasted with modern architectures like ConvTimeNet.
openaire +2 more sources
A time series causal model [PDF]
Cause-effect relations are central in economic analysis. Uncovering empirical cause-effect relations is one of the main research activities of empirical economics.
Chen, Pu
core +1 more source
Neuron‐derived MIF binds VCAM1 on gastric cancer cells and activates ERK/STAT3 signaling, leading to CXCL8 transcription and secretion. Tumor‐derived CXCL8 subsequently stimulates neuronal CXCR2 to enhance MIF production, establishing a self‐amplifying MIF–VCAM1–CXCL8 positive‐feedback loop that promotes perineural invasion, tumor progression, and ...
Xunjun Li +13 more
wiley +1 more source
DEformer: Dual Embedded Transformer for Multivariate Time Series Forecasting
Deep learning models have significantly addressed the challenges of multivariate time series forecasting. Recently, Transformer-based models which have primarily focused on either temporal or inter-variate (spatial) dependencies have demonstrated ...
Minje Kim, Suwon Lee, Sang-Min Choi
doaj +1 more source
Causal‐Guided Ultra‐Long‐Term Time Series Forecasting Via Anticipated Covariates
Often treated as unknown, information from the future remains underutilized.We demonstrate that in a coupled dynamical system, providing the future state of the effect enables accurate forecasting of the cause for a long timesteps. A time series forecasting paradigm that introduces anticipated covariates to represent such known future states is ...
Jintong Zhao +4 more
wiley +1 more source
A pneumatically actuated multi‐tissue microphysiological system is integrated with AI‐based machine vision and automatic sampling and replenishment systems. The platform allows for the emulation of translationally relevant long‐term pharmacokinetic exposure scenarios for multiple weeks while enabling longitudinal monitoring of response biomarkers ...
Jibbe Keulen +15 more
wiley +1 more source
25 Years of IIF Time Series Forecasting: A Selective Review [PDF]
We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one
Jan G. De Gooijer, Rob J. Hyndman
core
An integrated probiotic system is engineered to execute a programmed therapeutic cascade against inflammatory bowel disease. Upon pathological signals, the system sequentially performs ROS scavenging, tungsten release, and selective pathogen suppression, thereby breaking the oxidative stress–dysbiosis cycle to remodel gut microbiota and reinforce ...
Yang Yang +8 more
wiley +1 more source

