Network Informed Methods for High Dimensional Time Series

许多现实世界的数据集由随时间变化的数据组成，称为时间序列。例如，某一地区的日平均气温或某项金融资产的投资回报率。通常，我们对多个时间序列和它们之间的协同运动感兴趣。如果我们能够理解这种共同运动，就可以用它来改进预测。例如，在环境科学中，各种污染物随时间的浓度以及气象指标可用于预测质量，这与心血管和呼吸系统疾病的风险增加密切相关。在这个项目中，我们提出了一种新的多时间序列预测模型和估计技术。估计过程使用了网络科学的思想。我们的目标是了解所提出模型的数学特性，并研究其预测在各种应用数据集上是否优于其他模型。该项目属于EPSRC数学科学研究领域

Many real world data sets consist of data that changes over time, called a time series. For example, the daily average temperature in a particular region or the return on investment of some financial asset. Often,we are interested in multiple time series and the co-movement between them. If we can understand the co-movement, this can be used for improved predictions. For example, in environmental science, the concentration of various pollutants over time along with meteorological indicators can be used to predictair quality, which has a strong association with increased risk of cardiovascular and respiratory diseases. In this project we propose a new model and estimation technique for multiple time series prediction. The estimation procedure uses ideas from network science. We aim to understand the mathematical properties of the proposed model and investigate whether its predictions outperform other models on a variety ofapplication data sets. This project falls within the EPSRC MathematicalSciences research area