EPSRC Project Summary: New Methods for Network Time Series Analysis
EPSRC 项目摘要:网络时间序列分析的新方法
基本信息
- 批准号:2283002
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2019
- 资助国家:英国
- 起止时间:2019 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The rapidly increasing availability of multivariate time series data with explicit or implicit network structure have resulted in a heightened interest in network time series models for the purposes of forecasting or network structure inference. Such models have been used to forecast time series across a diverse array of research areas, from epidemiology to meteorology to social media networks. For example, the wind speed in a given area may depend on both past observations in the same location and those of its geographic neighbours, with varying lags and effect sizes. An accurate wind speed model may be a useful tool for deciding on the locations of new wind turbines, when it is not cost effective to collect the data at all candidate locations for long periods of time. The recently-developed generalised network autoregressive (GNAR) model provides both a flexible and highly parsimonious approach to the modelling of such data, by allowing dependence of the modelled series on an autoregressive component and neighbours across multiple covariate networks. My research aims to extend the GNAR modelling framework and develop new methods pertaining to network time series analysis in several areas. One extension would involve the development of novel algorithms for GNAR network structure inference in the absence of any network priors, allowing the treatment of all multivariate time series data sets as network time series. This would build on existing research for structural inference of Bayesian networks. Secondly, the GNAR model structure may be extended to incorporate node-specific exogenous time series regressors, which should lead to better forecasts and useful inferences when informative explanatory variables are available. Thirdly, my research will attempt to generalise network time series models to tensor-valued time series. For example, in the area of epidemiology, this would allow the parsimonious modelling of network time series where each location (or node in the network) possesses multiple time series representing case numbers, meteorological conditions and other factors relevant to disease transmission.Finally, I will examine the applications of deep learning to big network time series data sets, by using an initial network lifting preprocessing step to detrend and spatially decorrelate the data set. Of particular interest are extensions of `hybrid' deep learning architectures, such as the recently-developed Gaussian Process Long Short Term Memory (GP-LSTM) model. GP-LSTM uses a recurrent neural network to embed the kernel matrix of a Gaussian process and perform inference in a highly scalable fashion. As well as achieving state-of-the-art performance in time series forecasting tasks, the GP-LSTM allows for the straightforward estimation of the uncertainty in predictions of traditionally `opaque' neural networks. Furthermore, to my knowledge, the use of a network lifting scheme for feeding data into such deep learning models has not yet been examined in the machine learning literature.It is my hope that research in these areas will present novel contributions to the field of network time series analysis, that is to provide methodological tools to forecast multivariate time series using highly parsimonious models that exploit network structure. This project falls within the EPSRC Statistics and Applied Probability research area.________________________________________
随着具有显式或隐式网络结构的多变量时间序列数据的快速增长,人们对用于预测或网络结构推断的网络时间序列模型产生了极大的兴趣。这种模型已被用于预测从流行病学到气象学再到社交媒体网络等一系列不同研究领域的时间序列。例如,给定地区的风速可能取决于过去在同一地点的观测结果以及其地理上邻近地区的观测结果,具有不同的滞后和影响大小。当长时间收集所有候选地点的数据并不具有成本效益时,准确的风速模型可能是决定新风力涡轮机位置的有用工具。最近开发的广义网络自回归(GNAR)模型通过允许建模的序列依赖于多个协变量网络中的自回归分量和邻居,为此类数据的建模提供了一种灵活且高度节俭的方法。我的研究旨在扩展GNAR建模框架,并在几个领域开发与网络时间序列分析有关的新方法。一个扩展将涉及在没有任何网络先验的情况下开发用于GNAR网络结构推断的新算法,允许将所有多变量时间序列数据集作为网络时间序列来处理。这将建立在贝叶斯网络结构推理现有研究的基础上。其次,GNAR模型结构可以扩展到包括特定节点的外生时间序列回归变量,这应该会在有信息的解释变量时产生更好的预测和有用的推断。第三,将网络时间序列模型推广到张量值时间序列。例如,在流行病学领域,这将允许对网络时间序列进行简约建模,其中每个位置(或网络中的节点)拥有代表病例数量、气象条件和其他与疾病传播相关的因素的多个时间序列。最后,我将通过使用初始网络提升预处理步骤来对数据集进行趋势和空间去相关来研究深度学习在大型网络时间序列数据集上的应用。特别令人感兴趣的是“混合”深度学习体系结构的扩展,例如最近开发的高斯过程长期短期记忆(GP-LSTM)模型。GP-LSTM使用递归神经网络嵌入高斯过程的核矩阵,并以高度可扩展的方式进行推理。除了在时间序列预测任务中实现最先进的性能之外,GP-LSTM还允许对传统的不透明神经网络预测中的不确定性进行直接估计。此外,据我所知,使用网络提升方案将数据输入到这样的深度学习模型中还没有在机器学习文献中被研究过。我希望这些领域的研究将为网络时间序列分析领域提供新的贡献,即提供使用高度简约的模型来预测多变量时间序列的方法工具。本项目属于EPSRC统计和应用概率研究领域。_
项目成果
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其他文献
Internet-administered, low-intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE).
针对癌症儿童父母的互联网管理、低强度认知行为疗法:可行性试验 (ENGAGE)。
- DOI:
10.1002/cam4.5377 - 发表时间:
2023-03 - 期刊:
- 影响因子:4
- 作者:
- 通讯作者:
Differences in child and adolescent exposure to unhealthy food and beverage advertising on television in a self-regulatory environment.
在自我监管的环境中,儿童和青少年在电视上接触不健康食品和饮料广告的情况存在差异。
- DOI:
10.1186/s12889-023-15027-w - 发表时间:
2023-03-23 - 期刊:
- 影响因子:4.5
- 作者:
- 通讯作者:
The association between rheumatoid arthritis and reduced estimated cardiorespiratory fitness is mediated by physical symptoms and negative emotions: a cross-sectional study.
类风湿性关节炎与估计心肺健康降低之间的关联是由身体症状和负面情绪介导的:一项横断面研究。
- DOI:
10.1007/s10067-023-06584-x - 发表时间:
2023-07 - 期刊:
- 影响因子:3.4
- 作者:
- 通讯作者:
ElasticBLAST: accelerating sequence search via cloud computing.
ElasticBLAST:通过云计算加速序列搜索。
- DOI:
10.1186/s12859-023-05245-9 - 发表时间:
2023-03-26 - 期刊:
- 影响因子:3
- 作者:
- 通讯作者:
Amplified EQCM-D detection of extracellular vesicles using 2D gold nanostructured arrays fabricated by block copolymer self-assembly.
使用通过嵌段共聚物自组装制造的 2D 金纳米结构阵列放大 EQCM-D 检测细胞外囊泡。
- DOI:
10.1039/d2nh00424k - 发表时间:
2023-03-27 - 期刊:
- 影响因子:9.7
- 作者:
- 通讯作者:
的其他文献
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{{ truncateString('', 18)}}的其他基金
An implantable biosensor microsystem for real-time measurement of circulating biomarkers
用于实时测量循环生物标志物的植入式生物传感器微系统
- 批准号:
2901954 - 财政年份:2028
- 资助金额:
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利用人类肠道微生物群的多糖分解能力来开发环境可持续的洗碗解决方案
- 批准号:
2896097 - 财政年份:2027
- 资助金额:
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可以在颗粒材料中游动的机器人
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2780268 - 财政年份:2027
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Likelihood and impact of severe space weather events on the resilience of nuclear power and safeguards monitoring.
严重空间天气事件对核电和保障监督的恢复力的可能性和影响。
- 批准号:
2908918 - 财政年份:2027
- 资助金额:
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Proton, alpha and gamma irradiation assisted stress corrosion cracking: understanding the fuel-stainless steel interface
质子、α 和 γ 辐照辅助应力腐蚀开裂:了解燃料-不锈钢界面
- 批准号:
2908693 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Field Assisted Sintering of Nuclear Fuel Simulants
核燃料模拟物的现场辅助烧结
- 批准号:
2908917 - 财政年份:2027
- 资助金额:
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Studentship
Assessment of new fatigue capable titanium alloys for aerospace applications
评估用于航空航天应用的新型抗疲劳钛合金
- 批准号:
2879438 - 财政年份:2027
- 资助金额:
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Studentship
Developing a 3D printed skin model using a Dextran - Collagen hydrogel to analyse the cellular and epigenetic effects of interleukin-17 inhibitors in
使用右旋糖酐-胶原蛋白水凝胶开发 3D 打印皮肤模型,以分析白细胞介素 17 抑制剂的细胞和表观遗传效应
- 批准号:
2890513 - 财政年份:2027
- 资助金额:
-- - 项目类别:
Studentship
Understanding the interplay between the gut microbiome, behavior and urbanisation in wild birds
了解野生鸟类肠道微生物组、行为和城市化之间的相互作用
- 批准号:
2876993 - 财政年份:2027
- 资助金额:
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