Results 111 to 120 of about 93,376 (303)
The vector innovation structural time series framework: a simple approach to multivariate forecasting [PDF]
The vector innovation structural time series framework is proposed as a way of modelling a set of related time series. Like all multi-series approaches, the aim is to exploit potential inter-series dependencies to improve the fit and forecasts.
Rob J. Hyndman +2 more
core
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To delineate specific in vivo white matter pathology in neuronal intranuclear inclusion disease (NIID) using diffusion spectrum imaging (DSI) and define its clinical relevance. Methods DSI was performed on 42 NIID patients and 38 matched controls.
Kaiyan Jiang +10 more
wiley +1 more source
Time series transformer for tourism demand forecasting
Scientific ReportsAI-based methods have been widely adopted in tourism demand forecasting. However, current AI-based methods are weak in capturing long-term dependency, and most of them lack interpretability.
Siyuan Yi, Xing Chen, Chuanming Tang
doaj +1 more source
Least angle regression for time series forecasting with many predictors. [PDF]
Least Angle Regression(LARS)is a variable selection method with proven performance for cross-sectional data. In this paper, it is extended to time series forecasting with many predictors. The new method builds parsimonious forecast models,taking the time
Croux, Christophe, Gelper, Sarah
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CX3CL1 in Early Detection of Alzheimer's Disease: Plasma Dynamics Across Age and Disease Stages
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Backgrounds Alzheimer's disease (AD) is characterized by amyloid‐beta plaques, tau tangles, and neuroinflammation. C‐X3‐C motif chemokine ligand 1 (CX3CL1, also known as fractalkine), a neuroimmune chemokine implicated in AD pathogenesis, shows inconsistent alterations in plasma/serum across studies.
Ling Wang +6 more
wiley +1 more source
Implementation of bagging in time series forecasting
Российский технологический журналObjectives. The purpose of the article is to build different models of bagging, to compare the accuracy of their forecasts for the test period against standard models, and to draw conclusions about the possibility of further use of the bagging technique ...
Ia. V. Gramovich +2 more
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Cognitive decline is a disabling and variable feature of Parkinson disease (PD). While cholinergic system degeneration is linked to cognitive impairments in PD, most prior research reported cross‐sectional associations. We aimed to fill this gap by investigating whether baseline regional cerebral vesicular acetylcholine transporter ...
Taylor Brown +6 more
wiley +1 more source
Recent Advances in Time Series Forecasting Methods
Applied SciencesTime series forecasting has become a key decision-support tool, with broad applicability within a series of domains within the economic field, among other fields [...]
Camelia Delcea
doaj +1 more source
Decomposition by Causal Forces: A Procedure for Forecasting Complex Time Series [PDF]
Causal forces are a way of summarizing forecasters expectations about what will happen to a time series in the future. Contrary to the common assumption for extrapolation, time series are not always subject to consistent forces that point in the same ...
J. S. Armstrong
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Deep coupling network for multivariate time series forecasting
Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data.
Zhang, Qi +6 more
core +1 more source