Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Nason, Guy P."'
Data collected over networks can be modelled as noisy observations of an unknown function over the nodes of a graph or network structure, fully described by its nodes and their connections, the edges. In this context, function estimation has been pro
Externí odkaz:
http://arxiv.org/abs/2410.13693
Autor:
Nason, Guy P, Wei, James L.
This article combines wavelet analysis techniques with machine learning methods for univariate time series forecasting, focusing on three main contributions. Firstly, we consider the use of Daubechies wavelets with different numbers of vanishing mome
Externí odkaz:
http://arxiv.org/abs/2403.08630
Network time series are becoming increasingly relevant in the study of dynamic processes characterised by a known or inferred underlying network structure. Generalised Network Autoregressive (GNAR) models provide a parsimonious framework for exploiti
Externí odkaz:
http://arxiv.org/abs/2401.09381
Autor:
Liu, Hengxu, Nason, Guy
The original generalized network autoregressive models are poor for modelling count data as they are based on the additive and constant noise assumptions, which is usually inappropriate for count data. We introduce two new models (GNARI and NGNAR) fo
Externí odkaz:
http://arxiv.org/abs/2312.01944
Network time series are becoming increasingly important across many areas in science and medicine and are often characterised by a known or inferred underlying network structure, which can be exploited to make sense of dynamic phenomena that are ofte
Externí odkaz:
http://arxiv.org/abs/2312.00530
This article introduces the class of continuous time locally stationary wavelet processes. Continuous time models enable us to properly provide scale-based time series models for irregularly-spaced observations for the first time, while also permitti
Externí odkaz:
http://arxiv.org/abs/2310.12788
Accurate forecasting of the U.K. gross value added (GVA) is fundamental for measuring the growth of the U.K. economy. A common nonstationarity in GVA data, such as the ABML series, is its increase in variance over time due to inflation. Transformed o
Externí odkaz:
http://arxiv.org/abs/2303.07772
Autor:
Nason, Guy P, Wei, James L
Knowledge of the current state of economies, how they respond to COVID-19 mitigations and indicators, and what the future might hold for them is important. We use recently-developed generalised network autoregressive (GNAR) models, using trade-determ
Externí odkaz:
http://arxiv.org/abs/2107.07605
The classical regular and partial autocorrelation functions are powerful tools for stationary time series modelling and analysis. However, it is increasingly recognized that many time series are not stationary and the use of classical global autocorr
Externí odkaz:
http://arxiv.org/abs/2004.12716
Autor:
Nason, Guy P.
Spectral analysis characterises oscillatory time series behaviours such as cycles, but accurate estimation requires reasonable numbers of observations. Current COVID-19 time series for many countries are short: pre- and post-lockdown series are short
Externí odkaz:
http://arxiv.org/abs/2004.07696