Approaching Modern Problems in Multivariate Time Series via Network Modelling and State-Space Methods.

通过网络建模和状态空间方法解决多元时间序列中的现代问题。

基本信息

批准号：
2440162
负责人：
金额：
--
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2440162
关键词：
Approaching Modern Problems Multivariate Time

项目摘要

A sequence of time indexed observations, describing how a particular variable evolves with time, is referred to as a time series. In many applications, we record multiple observations of the same phenomena, leading to multiple different time series describing the same variable. The challenge of combining the differing time series into one model, which captures the dependencies and allows the prediction of future time series values, is a non-trivial task. This project is in collaboration with an industry partner, and aims to develop an effective method for the combination of such time series measurements in order to enable accurate predictions of future values.We will be addressing the question of how best to combine measurements from multiple different sources describing the same phenomenon, in addition to considering how dependent time series can be used to aid our predictions. This method should be able to deal with challenges presented by characteristics such as missing data, irregular sampling, unknown dependencies with other time series and uncertainty about the observations. An ideal solution will take into account the uncertainties associated with each individual time series, and will aim to minimise and quantify the uncertainty of the combined prediction. Furthermore, it will be able to determine the best possible combination of the individual time series, and adapt this combination in the case that some change occurs. The developed method will aim to be computationally efficient, and effective in substantive applications.

一系列时间索引观测值描述了特定变量随时间演变的方式，称为时间序列。在许多应用中，我们记录了相同现象的多个观察结果，导致多个不同的时间序列描述了相同的变量。将不同的时间序列组合到一个模型中的挑战，该模型捕获依赖关系并允许对未来时间序列值的预测，这是一项非平凡的任务。该项目与行业合作伙伴合作，旨在开发一种有效的方法来组合此类时间序列测量值，以便对未来价值的准确预测进行准确的预测。我们将解决如何最好地结合多个不同来源的测量来描述相同现象的测量值，此外还可以考虑如何使用依赖时间序列来帮助我们的预测。该方法应该能够应对特征所带来的挑战，例如丢失的数据，不规则采样，未知的依赖关系以及其他时间序列以及观察结果的不确定性。理想的解决方案将考虑到与每个单个时间序列相关的不确定性，并旨在最大程度地减少和量化合并预测的不确定性。此外，它将能够确定单个时间序列的最佳组合，并在发生某些变化的情况下适应此组合。开发的方法将旨在在计算上有效，并在实质性应用中有效。