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Themes in Time Series Analysis

时间序列分析的主题

基本信息

批准号：
3465-2012
负责人：
McLeod, AngusIan
金额：
$ 1.53万
依托单位：
University of Western Ontario
依托单位国家：
加拿大
项目类别：
Discovery Grants Program - Individual
财政年份：
2016
资助国家：
加拿大
起止时间：
2016-01-01 至 2017-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=592751
关键词：
Themes Time Series Analysis

项目摘要

Time series are sequences of consecutive data values. Example that I have worked with include the close price for a stock, mean daily temperature at a weather station, monthly average stratospheric ozone at a specified location, monthly number of consultations to a nephrologist for a specific condition, daily late-night automobile fatalities, successive heart interbeat intervals and many others. A time series method I developed was used to predict the number of years for stratospheric ozone to return to its previous levels (Technometrics, doi = 10.1.1.144.6621). My current research involves models for time series of gene expressions to detect periodicity and models for censored water quality time series. Some time series may exhibit strongly persistent autocorrelation as with hourly wind speed at a specific location. The detection and modeling of such strongly persistent series particularly with short time series is another current research area. Daily mean precipitation and temperature are available for many years at many regional weather stations. The modeling of such multiple time series taking into account the regional, seasonal and long term trend is an active research that I am involved in and it has applications for the management of water resources and reservoir systems, forest fire research and for use in downscaling with global climate change models. A new method for automatic multivariate spectral analysis is suggested. An interesting new type of nonsense correlation has also been discovered that can potentially invalidate the inferences in many types of statistical models. A method for detecting this problem and fixing it is being proposed.

时间序列是连续数据值的序列。我所研究的例子包括股票的收盘价、气象站的日平均气温、特定地点平流层臭氧的月平均值、肾病学家因特定情况而每月进行的咨询次数、每天深夜的汽车死亡人数、连续的心跳间隔以及许多其他数据。我开发的一种时间序列方法被用来预测平流层臭氧恢复到以前水平的年数（Technometrics，doi = 10.1.1.144.6621）。我目前的研究涉及基因表达时间序列的模型，以检测周期性和审查水质时间序列的模型。一些时间序列可能会表现出强烈的持续自相关性，如特定位置的每小时风速。对这种强持续序列的检测和建模，特别是对短时间序列的检测和建模是当前的另一个研究领域。许多区域气象站提供多年的日平均降水量和温度。考虑到区域，季节性和长期趋势的这种多时间序列的建模是我参与的一项积极研究，它适用于水资源和水库系统的管理，森林火灾研究和全球气候变化模型的降尺度。本文提出了一种自动多元光谱分析的新方法。还发现了一种有趣的新型无意义相关性，它可能会使许多类型的统计模型中的推论无效。正在提出一种检测此问题并修复它的方法。